[
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"path": ".gitignore",
"content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\nsftp-config.json\n\n.DS_Store\n\n*.py.swp\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy \n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/"
},
{
"path": "README.md",
"content": "# CorefQA: Coreference Resolution as Query-based Span Prediction\n\nThe repository contains the code of the recent research advances in [Shannon.AI](http://www.shannonai.com). Please post github issues or email xiaoya_li@shannonai.com for relevant questions.\n\n\n\n**CorefQA: Coreference Resolution as Query-based Span Prediction**
\nWei Wu, Fei Wang, Arianna Yuan, Fei Wu and Jiwei Li
\nIn ACL 2020. [paper](https://arxiv.org/abs/1911.01746)
\nIf you find this repo helpful, please cite the following:\n```latex\n@article{wu2019coreference,\n title={Coreference Resolution as Query-based Span Prediction},\n author={Wu, Wei and Wang, Fei and Yuan, Arianna and Wu, Fei and Li, Jiwei},\n journal={arXiv preprint arXiv:1911.01746},\n year={2019}\n}\n```\n\n\n## Contents \n- [Overview](#overview)\n- [Hardware Requirements](#hardware-requirements)\n- [Install Package Dependencies](#install-package-dependencies)\n- [Data Preprocess](#data-preprocess)\n- [Download Pretrained MLM](#download-pretrained-mlm)\n- [Training](#training)\n - [Finetune the SpanBERT Model on the Combination of Squad and Quoref Datasets](#finetune-the-spanbert-model-on-the-combination-of-squad-and-quoref-datasets)\n - [Train the CorefQA Model on the CoNLL-2012 Coreference Resolution Task](#train-the-corefqa-model-on-the-conll-2012-coreference-resolution-task)\n- [Evaluation and Prediction](#evaluation-and-prediction)\n- [Download the Final CorefQA Model](#download-the-final-corefqa-model)\n- [Descriptions of Directories](#descriptions-of-directories)\n- [Acknowledgement](#acknowledgement)\n- [Useful Materials](#useful-materials)\n- [Contact](#contact)\n\n\n## Overview \nThe model introduces +3.5 (83.1) F1 performance boost over previous SOTA coreference models on the CoNLL benchmark. The current codebase is written in Tensorflow. We plan to release the PyTorch version soon. The current code version only supports training on TPUs and testing on GPUs (due to the annoying features of TF and TPUs). You thus have to bear the trouble of transferring all saved checkpoints from TPUs to GPUs for evaluation (we will fix this soon). Please follow the parameter setting in the log directionary to reproduce the performance. \n\n\n| Model | F1 (%) |\n| -------------- |:------:|\n| Previous SOTA (Joshi et al., 2019a) | 79.6 |\n| CorefQA + SpanBERT-large | 83.1 |\n\n\n## Hardware Requirements\nTPU for training: Cloud TPU v3-8 device (128G memory) with Tensorflow 1.15 Python 3.5 \n\nGPU for evaluation: with CUDA 10.0 Tensorflow 1.15 Python 3.5\n\n## Install Package Dependencies\n \n```shell\n$ python3 -m pip install --user virtualenv\n$ virtualenv --python=python3.5 ~/corefqa_venv\n$ source ~/corefqa_venv/bin/activate\n$ cd CorefQA\n$ pip install -r requirements.txt\n# If you are using TPU, please run the following commands:\n$ pip install --upgrade google-api-python-client \n$ pip install --upgrade oauth2client \n```\n\n## Data Preprocess \n\n1) Download the offical released [Ontonotes 5.0 (LDC2013T19)](https://catalog.ldc.upenn.edu/LDC2013T19).
\n2) Preprocess Ontonotes5 annotations files for the CoNLL-2012 coreference resolution task.
\nRun the command with **Python 2**\n`bash ./scripts/data/preprocess_ontonotes_annfiles.sh `
\nand it will create `{train/dev/test}.{language}.v4_gold_conll` files in the directory ``.
\n`` can be `english`, `arabic` or `chinese`. In this paper, we set `` to `english`.
\nIf you want to use **Python 3**, please refer to the\n[guideline](https://github.com/huggingface/neuralcoref/blob/master/neuralcoref/train/training.md#get-the-data)
\n3) Generate TFRecord files for experiments.
\nRun the command with **Python 3** `bash ./scripts/data/generate_tfrecord_dataset.sh `\nand it will create `{train/dev/test}.overlap.corefqa.{language}.tfrecord` files in the directory ``.
\n\n## Download Pretrained MLM\nIn our experiments, we used pretrained mask language models to initialize the mention_proposal and corefqa models. \n\n1) Download the pretrained models.
\nRun `bash ./scripts/data/download_pretrained_mlm.sh ` to download and unzip the pretrained mlm models.
\n`` shoule take the value of `[bert_base, bert_large, spanbert_base, spanbert_large, bert_tiny]`.\n\n- `bert_base, bert_large, spanbert_base, spanbert_large` are trained with a cased(upppercase and lowercase tokens) vocabulary. Should use the cased train/dev/test coreference datasets. \n- `bert_tiny` is trained with a uncased(lowercase tokens) vocabulary. We use the tinyBERT model for fast debugging. Should use the uncased train/dev/test coreference datasets.
\n\n2) Transform SpanBERT from `Pytorch` to `Tensorflow`.
\n\nAfter downloading `bert_` to `_tf_dir>` and `spanbert_` to `_pytorch_dir>`, you can start transforming the SpanBERT model to Tensorflow and the model is saved to the directory ``. `` should take the value of `[base, large]`.
\n\nWe need to tranform the SpanBERT checkpoints from Pytorch to TF because the offical relased models were trained with Pytorch. \nRun `bash ./scripts/data/transform_ckpt_pytorch_to_tf.sh _pytorch_dir> _tf_dir> ` \nand the `` in TF will be saved in ``.\n\n- `` should take the value of `[spanbert_base, spanbert_large]`. \n- `` indicates that the `bert_model.ckpt` in the `_tf_dir>` should have the same scale(base, large) to the `bert_model.bin` in `_pytorch_dir>`.\n\n\n## Training \n\nFollow the pipeline described in the paper, you need to:
\n1) load a pretrained SpanBERT model.
\n2) finetune the SpanBERT model on the combination of Squad and Quoref datasets.
\n3) pretrain the mention proposal model on the coref dataset.
\n4) jointly train the mention proposal model and the mention linking model.
\n \n**Notice:** We provide the options of both pretraining these models yourself and loading the our pretrained models for 2) and 3).
\n\n### Finetune the SpanBERT Model on the Combination of Squad and Quoref Datasets\nWe finetune the SpanBERT model on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) and [Quoref](https://allennlp.org/quoref) QA tasks for data augmentation before the coreference resolution task. \n\n1. You can directly download the pretrained model on the datasets. \nDownload Data Augmentation Models on Squad and Quoref [link](https://www.dropbox.com/s/lqjc6kfe0w34jt0/finetune_spanbert_large_squad2.tar.gz?dl=0)
\nRun `./scripts/data/download_squad2_finetune_model.sh ` to download finetuned SpanBERT on SQuAD2.0.
\nThe `` should take the value of `[base, large]`.
\nThe `` is the path to save finetuned spanbert on SQuAD2.0 datasets.
\n\n\n2. Or start to finetune the SpanBERT model on QA tasks yourself. \n- Download SQuAD 2.0 [train](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json) and [dev](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json) sets. \n- Download Quoref [train and dev](https://quoref-dataset.s3-us-west-2.amazonaws.com/train_and_dev/quoref-train-dev-v0.1.zip) sets.\n- Finetune the SpanBERT model on Google Could V3-8 TPU. \n\nFor Squad 2.0, Run the script in [./script/model/squad_tpu.sh](https://github.com/ShannonAI/CorefQA/blob/master/scripts/models/squad_tpu.sh)\n ```bash \n \n REPO_PATH=/home/shannon/coref-tf\n export TPU_NAME=tf-tpu\n export PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\n SQUAD_DIR=gs://qa_tasks/squad2\n BERT_DIR=gs://pretrained_mlm_checkpoint/spanbert_large_tf\n OUTPUT_DIR=gs://corefqa_output_squad/spanbert_large_squad2_2e-5 \n\n python3 ${REPO_PATH}/run/run_squad.py \\\n --vocab_file=$BERT_DIR/vocab.txt \\\n --bert_config_file=$BERT_DIR/bert_config.json \\\n --init_checkpoint=$BERT_DIR/bert_model.ckpt \\\n --do_train=True \\\n --train_file=$SQUAD_DIR/train-v2.0.json \\\n --do_predict=True \\\n --predict_file=$SQUAD_DIR/dev-v2.0.json \\\n --train_batch_size=8 \\\n --learning_rate=2e-5 \\\n --num_train_epochs=4.0 \\\n --max_seq_length=384 \\\n --do_lower_case=False \\\n --doc_stride=128 \\\n --output_dir=${OUTPUT_DIR} \\\n --use_tpu=True \\\n --tpu_name=$TPU_NAME \\\n --version_2_with_negative=True\n ```\nAfter getting the best model (choose based on the performance on dev set) on `SQuAD2.0`, you should start finetuning the saved model on `Quoref`.
\n\nRun the script in [./script/model/quoref_tpu.sh](https://github.com/ShannonAI/CorefQA/blob/master/scripts/models/quoref_tpu.sh) \n ```bash \n \n REPO_PATH=/home/shannon/coref-tf\n export TPU_NAME=tf-tpu\n export PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\n QUOREF_DIR=gs://qa_tasks/quoref\n BERT_DIR=gs://corefqa_output_squad/panbert_large_squad2_2e-5\n OUTPUT_DIR=gs://corefqa_output_quoref/spanbert_large_squad2_best_quoref_3e-5 \n\n python3 ${REPO_PATH}/run_quoref.py \\\n --vocab_file=$BERT_DIR/vocab.txt \\\n --bert_config_file=$BERT_DIR/bert_config.json \\\n --init_checkpoint=$BERT_DIR/best_bert_model.ckpt \\\n --do_train=True \\\n --train_file=$QUOREF_DIR/quoref-train-v0.1.json \\\n --do_predict=True \\\n --predict_file=$QUOREF_DIR/quoref-dev-v0.1.json \\\n --train_batch_size=8 \\\n --learning_rate=3e-5 \\\n --num_train_epochs=5 \\\n --max_seq_length=384 \\\n --do_lower_case=False \\\n --doc_stride=128 \\\n --output_dir=${OUTPUT_DIR} \\\n --use_tpu=True \\\n --tpu_name=$TPU_NAME \n ```\nWe use the best model (choose based on the performance on DEV set) on `Quoref` to initialize the CorefQA Model. \n \n### Train the CorefQA Model on the CoNLL-2012 Coreference Resolution Task \n1.1 Your can you can download the pre-trained mention proposal model (including [model](https://storage.googleapis.com/public_model_checkpoints/mention_proposal/model.ckpt-22000.data-00000-of-00001), [meta](https://storage.googleapis.com/public_model_checkpoints/mention_proposal/model.ckpt-22000.meta) and [index](https://storage.googleapis.com/public_model_checkpoints/mention_proposal/model.ckpt-22000.index)). \n\n1.2. Or train the mention proposal model yourself. \n\nThe script can be found in [./script/model/mention_tpu.sh](https://github.com/ShannonAI/CorefQA/blob/master/scripts/models/mention_tpu.sh).\n\n```bash \n\nREPO_PATH=/home/shannon/coref-tf\nexport PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\nexport TPU_NAME=tf-tpu\nexport TPU_ZONE=europe-west4-a\nexport GCP_PROJECT=xiaoyli-20-01-4820\n\nBERT_DIR=gs://corefqa_output_quoref/spanbert_large_squad2_best_quoref_1e-5\nDATA_DIR=gs://corefqa_data/final_overlap_384_6\nOUTPUT_DIR=gs://corefqa_output_mention_proposal/squad_quoref_large_384_6_1e5_8_0.2\n\npython3 ${REPO_PATH}/run/run_mention_proposal.py \\\n--output_dir=$OUTPUT_DIR \\\n--bert_config_file=$BERT_DIR/bert_config.json \\\n--init_checkpoint=$BERT_DIR/bert_model.ckpt \\\n--vocab_file=$BERT_DIR/vocab.txt \\\n--logfile_path=$OUTPUT_DIR/train.log \\\n--num_epochs=8 \\\n--keep_checkpoint_max=50 \\\n--save_checkpoints_steps=500 \\\n--train_file=$DATA_DIR/train.corefqa.english.tfrecord \\\n--dev_file=$DATA_DIR/dev.corefqa.english.tfrecord \\\n--test_file=$DATA_DIR/test.corefqa.english.tfrecord \\\n--do_train=True \\\n--do_eval=False \\\n--do_predict=False \\\n--learning_rate=1e-5 \\\n--dropout_rate=0.2 \\\n--mention_threshold=0.5 \\\n--hidden_size=1024 \\\n--num_docs=5604 \\\n--window_size=384 \\\n--num_window=6 \\\n--max_num_mention=60 \\\n--start_end_share=False \\\n--loss_start_ratio=0.3 \\\n--loss_end_ratio=0.3 \\\n--loss_span_ratio=0.3 \\\n--use_tpu=True \\\n--tpu_name=$TPU_NAME \\\n--tpu_zone=$TPU_ZONE \\\n--gcp_project=$GCP_PROJECT \\\n--num_tpu_cores=1 \\\n--seed=2333\n```\n\n2. Jointly train the mention proposal model and linking model on CoNLL-12.
\n\nAfter getting the best mention proposal model on the dev set, start jointly training the mention proposal and linking tasks. \n\nRun and the script can be found in [./script/model/corefqa_tpu.sh](https://github.com/ShannonAI/CorefQA/blob/master/scripts/models/corefqa_tpu.sh)\n\n```bash\n\nREPO_PATH=/home/shannon/coref-tf\nexport PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\nexport TPU_NAME=tf-tpu\nexport TPU_ZONE=europe-west4-a\nexport GCP_PROJECT=xiaoyli-20-01-4820\n\nBERT_DIR=gs://corefqa_output_mention_proposal/output_bertlarge\nDATA_DIR=gs://corefqa_data/final_overlap_384_6\nOUTPUT_DIR=gs://corefqa_output_corefqa/squad_quoref_mention_large_384_6_8e4_8_0.2\n\npython3 ${REPO_PATH}/run/run_corefqa.py \\\n--output_dir=$OUTPUT_DIR \\\n--bert_config_file=$BERT_DIR/bert_config.json \\\n--init_checkpoint=$BERT_DIR/best_bert_model.ckpt \\\n--vocab_file=$BERT_DIR/vocab.txt \\\n--logfile_path=$OUTPUT_DIR/train.log \\\n--num_epochs=8 \\\n--keep_checkpoint_max=50 \\\n--save_checkpoints_steps=500 \\\n--train_file=$DATA_DIR/train.corefqa.english.tfrecord \\\n--dev_file=$DATA_DIR/dev.corefqa.english.tfrecord \\\n--test_file=$DATA_DIR/test.corefqa.english.tfrecord \\\n--do_train=True \\\n--do_eval=False \\\n--do_predict=False \\\n--learning_rate=8e-4 \\\n--dropout_rate=0.2 \\\n--mention_threshold=0.5 \\\n--hidden_size=1024 \\\n--num_docs=5604 \\\n--window_size=384 \\\n--num_window=6 \\\n--max_num_mention=50 \\\n--start_end_share=False \\\n--max_span_width=10 \\\n--max_candiate_mentions=100 \\\n--top_span_ratio=0.2 \\\n--max_top_antecedents=30 \\\n--max_query_len=150 \\\n--max_context_len=150 \\\n--sec_qa_mention_score=False \\\n--use_tpu=True \\\n--tpu_name=$TPU_NAME \\\n--tpu_zone=$TPU_ZONE \\\n--gcp_project=$GCP_PROJECT \\\n--num_tpu_cores=1 \\\n--seed=2333\n```\n\n## Evaluation and Prediction\n\nCurrently, the evaluation is conducted on a set of saved checkpoints after the training process, and DO NOT support evaluation during training. Please transfer all checkpoints (the output directory is set `--output_dir=` when running the `run_.py`) from TPUs to GPUs for evaluation. \nThis can be achieved by downloading the output directory from the Google Cloud Storage.
\n\n\nThe performance on the test set is obtained by using the model achieving the highest F1-score on the dev set.
\nSet `--do_eval=True`、 `--do_train=False` and `--do_predict=False` to `run_.py` and start the evaluation process on a set of saved checkpoints. And other parameters should be the same with the training process.\n`` should take the value of `[mention_proposal, corefqa]`.
\n\nThe codebase also provides the option of evaluating a single model/checkpoint. Please set `--do_eval=False`、 `--do_train=False` and `--do_predict=True` to `run_.py` with the checkpoint path `--eval_checkpoint=`.\n`` should take the value of `[mention_proposal, corefqa]`.\n
\n \n## Download the Final CorefQA Model\nYou can download the final CorefQA model at [link](https://drive.google.com/file/d/1RPYsS2dDxYyii7-3NkBNG7VtuA96NBLf/view?usp=sharing) and follow the instructions in the prediciton to obtain the score reported in the paper. \n\n\n## Descriptions of Directories\n\nName | Descriptions \n----------- | ------------- \nbert | BERT modules (model,tokenizer,optimization) ref to the `google-research/bert` repository. \nconll-2012 | offical evaluation scripts for CoNLL2012 shared task.\ndata_utils | modules for processing training data. \nfunc_builders | the input dataloader and model constructor for CorefQA.\nlogs | the log files in our experiments. \nmodels | an implementation of CorefQA/MentionProposal models based on TF.\nrun | modules for data preparation and training models.\nscripts/data | scripts for data preparation and loading pretrained models.\nscripts/models | scripts for {train/evaluate} {mention_proposal/corefqa} models on {TPU/GPU}. \nutils | modules including metrics、optimizers. \n\n\n\n## Acknowledgement\n\nMany thanks to `Yuxian Meng` and the previous work `https://github.com/mandarjoshi90/coref`.\n\n## Useful Materials\n\n- TPU Quick Start [link](https://cloud.google.com/tpu/docs/quickstart)\n- TPU Available Operations [link](https://cloud.google.com/tpu/docs/tensorflow-ops)\n\n## Contact \n\nFeel free to discuss papers/code with us through issues/emails!\n"
},
{
"path": "bert/__init__.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n"
},
{
"path": "bert/modeling.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"The main BERT model and related functions.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport copy\nimport json\nimport math\nimport re\n\nimport six\nimport tensorflow as tf\n\n\nclass BertConfig(object):\n \"\"\"Configuration for `BertModel`.\"\"\"\n\n def __init__(self,\n vocab_size,\n hidden_size=768,\n num_hidden_layers=12,\n num_attention_heads=12,\n intermediate_size=3072,\n hidden_act=\"gelu\",\n hidden_dropout_prob=0.1,\n attention_probs_dropout_prob=0.1,\n max_position_embeddings=512,\n type_vocab_size=16,\n initializer_range=0.02):\n \"\"\"Constructs BertConfig.\n\n Args:\n vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.\n hidden_size: Size of the encoder layers and the pooler layer.\n num_hidden_layers: Number of hidden layers in the Transformer encoder.\n num_attention_heads: Number of attention heads for each attention layer in\n the Transformer encoder.\n intermediate_size: The size of the \"intermediate\" (i.e., feed-forward)\n layer in the Transformer encoder.\n hidden_act: The non-linear activation function (function or string) in the\n encoder and pooler.\n hidden_dropout_prob: The dropout probability for all fully connected\n layers in the embeddings, encoder, and pooler.\n attention_probs_dropout_prob: The dropout ratio for the attention\n probabilities.\n max_position_embeddings: The maximum sequence length that this model might\n ever be used with. Typically set this to something large just in case\n (e.g., 512 or 1024 or 2048).\n type_vocab_size: The vocabulary size of the `token_type_ids` passed into\n `BertModel`.\n initializer_range: The stdev of the truncated_normal_initializer for\n initializing all weight matrices.\n \"\"\"\n self.vocab_size = vocab_size\n self.hidden_size = hidden_size\n self.num_hidden_layers = num_hidden_layers\n self.num_attention_heads = num_attention_heads\n self.hidden_act = hidden_act\n self.intermediate_size = intermediate_size\n self.hidden_dropout_prob = hidden_dropout_prob\n self.attention_probs_dropout_prob = attention_probs_dropout_prob\n self.max_position_embeddings = max_position_embeddings\n self.type_vocab_size = type_vocab_size\n self.initializer_range = initializer_range\n\n @classmethod\n def from_dict(cls, json_object):\n \"\"\"Constructs a `BertConfig` from a Python dictionary of parameters.\"\"\"\n config = BertConfig(vocab_size=None)\n for (key, value) in six.iteritems(json_object):\n config.__dict__[key] = value\n return config\n\n @classmethod\n def from_json_file(cls, json_file):\n \"\"\"Constructs a `BertConfig` from a json file of parameters.\"\"\"\n with tf.gfile.GFile(json_file, \"r\") as reader:\n text = reader.read()\n return cls.from_dict(json.loads(text))\n\n def to_dict(self):\n \"\"\"Serializes this instance to a Python dictionary.\"\"\"\n output = copy.deepcopy(self.__dict__)\n return output\n\n def to_json_string(self):\n \"\"\"Serializes this instance to a JSON string.\"\"\"\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"\n\n\nclass BertModel(object):\n \"\"\"BERT model (\"Bidirectional Embedding Representations from a Transformer\").\n\n Example usage:\n\n ```python\n # Already been converted into WordPiece token ids\n input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])\n input_mask = tf.constant([[1, 1, 1], [1, 1, 0]])\n token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])\n\n config = modeling.BertConfig(vocab_size=32000, hidden_size=512,\n num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)\n\n model = modeling.BertModel(config=config, is_training=True,\n input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids)\n\n label_embeddings = tf.get_variable(...)\n pooled_output = model.get_pooled_output()\n logits = tf.matmul(pooled_output, label_embeddings)\n ...\n ```\n \"\"\"\n\n def __init__(self,\n config,\n is_training,\n input_ids,\n input_mask=None,\n token_type_ids=None,\n use_one_hot_embeddings=True,\n scope=None):\n \"\"\"Constructor for BertModel.\n\n Args:\n config: `BertConfig` instance.\n is_training: bool. rue for training model, false for eval model. Controls\n whether dropout will be applied.\n input_ids: int32 Tensor of shape [batch_size, seq_length].\n input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].\n token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].\n use_one_hot_embeddings: (optional) bool. Whether to use one-hot word\n embeddings or tf.embedding_lookup() for the word embeddings. On the TPU,\n it is must faster if this is True, on the CPU or GPU, it is faster if\n this is False.\n scope: (optional) variable scope. Defaults to \"bert\".\n\n Raises:\n ValueError: The config is invalid or one of the input tensor shapes\n is invalid.\n \"\"\"\n config = copy.deepcopy(config)\n config.hidden_dropout_prob = tf.to_float(is_training) * config.hidden_dropout_prob\n config.attention_probs_dropout_prob = tf.to_float(is_training) * config.attention_probs_dropout_prob\n # config.hidden_dropout_prob = tf.Print(config.hidden_dropout_prob, [config.hidden_dropout_prob], 'hdden')\n # if not is_training:\n # config.hidden_dropout_prob = 0.0\n # config.attention_probs_dropout_prob = 0.0\n\n input_shape = get_shape_list(input_ids, expected_rank=2)\n batch_size = input_shape[0]\n seq_length = input_shape[1]\n\n if input_mask is None:\n input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32)\n\n if token_type_ids is None:\n token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32)\n\n with tf.variable_scope(scope, default_name=\"bert\", reuse=tf.AUTO_REUSE):\n with tf.variable_scope(\"embeddings\"):\n # Perform embedding lookup on the word ids.\n (self.embedding_output, self.embedding_table) = embedding_lookup(\n input_ids=input_ids,\n vocab_size=config.vocab_size,\n embedding_size=config.hidden_size,\n initializer_range=config.initializer_range,\n word_embedding_name=\"word_embeddings\",\n use_one_hot_embeddings=use_one_hot_embeddings)\n\n # Add positional embeddings and token type embeddings, then layer\n # normalize and perform dropout.\n self.embedding_output = embedding_postprocessor(\n input_tensor=self.embedding_output,\n use_token_type=True,\n token_type_ids=token_type_ids,\n token_type_vocab_size=config.type_vocab_size,\n token_type_embedding_name=\"token_type_embeddings\",\n use_position_embeddings=True,\n position_embedding_name=\"position_embeddings\",\n initializer_range=config.initializer_range,\n max_position_embeddings=config.max_position_embeddings,\n dropout_prob=config.hidden_dropout_prob)\n\n with tf.variable_scope(\"encoder\"):\n # This converts a 2D mask of shape [batch_size, seq_length] to a 3D\n # mask of shape [batch_size, seq_length, seq_length] which is used\n # for the attention scores.\n attention_mask = create_attention_mask_from_input_mask(\n input_ids, input_mask)\n\n # Run the stacked transformer.\n # `sequence_output` shape = [batch_size, seq_length, hidden_size].\n self.all_encoder_layers = transformer_model(\n input_tensor=self.embedding_output,\n attention_mask=attention_mask,\n hidden_size=config.hidden_size,\n num_hidden_layers=config.num_hidden_layers,\n num_attention_heads=config.num_attention_heads,\n intermediate_size=config.intermediate_size,\n intermediate_act_fn=get_activation(config.hidden_act),\n hidden_dropout_prob=config.hidden_dropout_prob,\n attention_probs_dropout_prob=config.attention_probs_dropout_prob,\n initializer_range=config.initializer_range,\n do_return_all_layers=True)\n\n self.sequence_output = self.all_encoder_layers[-1]\n # The \"pooler\" converts the encoded sequence tensor of shape\n # [batch_size, seq_length, hidden_size] to a tensor of shape\n # [batch_size, hidden_size]. This is necessary for segment-level\n # (or segment-pair-level) classification tasks where we need a fixed\n # dimensional representation of the segment.\n with tf.variable_scope(\"pooler\"):\n # We \"pool\" the model by simply taking the hidden state corresponding\n # to the first token. We assume that this has been pre-trained\n first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1)\n self.pooled_output = tf.layers.dense(\n first_token_tensor,\n config.hidden_size,\n activation=tf.tanh,\n kernel_initializer=create_initializer(config.initializer_range))\n\n def get_pooled_output(self):\n return self.pooled_output\n\n def get_sequence_output(self):\n \"\"\"Gets final hidden layer of encoder.\n\n Returns:\n float Tensor of shape [batch_size, seq_length, hidden_size] corresponding\n to the final hidden of the transformer encoder.\n \"\"\"\n return self.sequence_output\n\n def get_all_encoder_layers(self):\n return self.all_encoder_layers\n\n def get_embedding_output(self):\n \"\"\"Gets output of the embedding lookup (i.e., input to the transformer).\n\n Returns:\n float Tensor of shape [batch_size, seq_length, hidden_size] corresponding\n to the output of the embedding layer, after summing the word\n embeddings with the positional embeddings and the token type embeddings,\n then performing layer normalization. This is the input to the transformer.\n \"\"\"\n return self.embedding_output\n\n def get_embedding_table(self):\n return self.embedding_table\n\n\ndef gelu(input_tensor):\n \"\"\"Gaussian Error Linear Unit.\n\n This is a smoother version of the RELU.\n Original paper: https://arxiv.org/abs/1606.08415\n\n Args:\n input_tensor: float Tensor to perform activation.\n\n Returns:\n `input_tensor` with the GELU activation applied.\n \"\"\"\n cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))\n return input_tensor * cdf\n\n\ndef get_activation(activation_string):\n \"\"\"Maps a string to a Python function, e.g., \"relu\" => `tf.nn.relu`.\n\n Args:\n activation_string: String name of the activation function.\n\n Returns:\n A Python function corresponding to the activation function. If\n `activation_string` is None, empty, or \"linear\", this will return None.\n If `activation_string` is not a string, it will return `activation_string`.\n\n Raises:\n ValueError: The `activation_string` does not correspond to a known\n activation.\n \"\"\"\n\n # We assume that anything that\"s not a string is already an activation\n # function, so we just return it.\n if not isinstance(activation_string, six.string_types):\n return activation_string\n\n if not activation_string:\n return None\n\n act = activation_string.lower()\n if act == \"linear\":\n return None\n elif act == \"relu\":\n return tf.nn.relu\n elif act == \"gelu\":\n return gelu\n elif act == \"tanh\":\n return tf.tanh\n else:\n raise ValueError(\"Unsupported activation: %s\" % act)\n\n\ndef get_assignment_map_from_checkpoint(tvars, init_checkpoint):\n \"\"\"Compute the union of the current variables and checkpoint variables.\"\"\"\n initialized_variable_names = {}\n\n name_to_variable = collections.OrderedDict()\n for var in tvars:\n name = var.name\n m = re.match(\"^(.*):\\\\d+$\", name)\n if m is not None:\n name = m.group(1)\n name_to_variable[name] = var\n\n init_vars = tf.train.list_variables(init_checkpoint) # checkpoint variables,\n\n assignment_map = collections.OrderedDict()\n for x in init_vars:\n (name, var) = (x[0], x[1])\n if name not in name_to_variable:\n continue\n assignment_map[name] = name\n initialized_variable_names[name] = 1\n initialized_variable_names[name + \":0\"] = 1\n\n return assignment_map, initialized_variable_names\n\n\ndef dropout(input_tensor, dropout_prob):\n \"\"\"Perform dropout.\n\n Args:\n input_tensor: float Tensor.\n dropout_prob: Python float. The probability of dropping out a value (NOT of\n *keeping* a dimension as in `tf.nn.dropout`).\n\n Returns:\n A version of `input_tensor` with dropout applied.\n \"\"\"\n if dropout_prob is None or dropout_prob == 0.0:\n return input_tensor\n\n output = tf.nn.dropout(input_tensor, 1.0 - dropout_prob)\n return output\n\n\ndef layer_norm(input_tensor, name=None):\n \"\"\"Run layer normalization on the last dimension of the tensor.\"\"\"\n return tf.contrib.layers.layer_norm(\n inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)\n\n\ndef layer_norm_and_dropout(input_tensor, dropout_prob, name=None):\n \"\"\"Runs layer normalization followed by dropout.\"\"\"\n output_tensor = layer_norm(input_tensor, name)\n output_tensor = dropout(output_tensor, dropout_prob)\n return output_tensor\n\n\ndef create_initializer(initializer_range=0.02):\n \"\"\"Creates a `truncated_normal_initializer` with the given range.\"\"\"\n return tf.truncated_normal_initializer(stddev=initializer_range)\n\n\ndef embedding_lookup(input_ids,\n vocab_size,\n embedding_size=128,\n initializer_range=0.02,\n word_embedding_name=\"word_embeddings\",\n use_one_hot_embeddings=False):\n \"\"\"Looks up words embeddings for id tensor.\n\n Args:\n input_ids: int32 Tensor of shape [batch_size, seq_length] containing word\n ids.\n vocab_size: int. Size of the embedding vocabulary.\n embedding_size: int. Width of the word embeddings.\n initializer_range: float. Embedding initialization range.\n word_embedding_name: string. Name of the embedding table.\n use_one_hot_embeddings: bool. If True, use one-hot method for word\n embeddings. If False, use `tf.nn.embedding_lookup()`. One hot is better\n for TPUs.\n\n Returns:\n float Tensor of shape [batch_size, seq_length, embedding_size].\n \"\"\"\n # This function assumes that the input is of shape [batch_size, seq_length,\n # num_inputs].\n #\n # If the input is a 2D tensor of shape [batch_size, seq_length], we\n # reshape to [batch_size, seq_length, 1].\n if input_ids.shape.ndims == 2:\n input_ids = tf.expand_dims(input_ids, axis=[-1])\n\n embedding_table = tf.get_variable(\n name=word_embedding_name,\n shape=[vocab_size, embedding_size],\n initializer=create_initializer(initializer_range))\n\n if use_one_hot_embeddings:\n flat_input_ids = tf.reshape(input_ids, [-1])\n one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)\n output = tf.matmul(one_hot_input_ids, embedding_table)\n else:\n output = tf.nn.embedding_lookup(embedding_table, input_ids)\n\n input_shape = get_shape_list(input_ids)\n\n output = tf.reshape(output,\n input_shape[0:-1] + [input_shape[-1] * embedding_size])\n return (output, embedding_table)\n\n\ndef embedding_postprocessor(input_tensor,\n use_token_type=False,\n token_type_ids=None,\n token_type_vocab_size=16,\n token_type_embedding_name=\"token_type_embeddings\",\n use_position_embeddings=True,\n position_embedding_name=\"position_embeddings\",\n initializer_range=0.02,\n max_position_embeddings=512,\n dropout_prob=0.1):\n \"\"\"Performs various post-processing on a word embedding tensor.\n\n Args:\n input_tensor: float Tensor of shape [batch_size, seq_length,\n embedding_size].\n use_token_type: bool. Whether to add embeddings for `token_type_ids`.\n token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].\n Must be specified if `use_token_type` is True.\n token_type_vocab_size: int. The vocabulary size of `token_type_ids`.\n token_type_embedding_name: string. The name of the embedding table variable\n for token type ids.\n use_position_embeddings: bool. Whether to add position embeddings for the\n position of each token in the sequence.\n position_embedding_name: string. The name of the embedding table variable\n for positional embeddings.\n initializer_range: float. Range of the weight initialization.\n max_position_embeddings: int. Maximum sequence length that might ever be\n used with this model. This can be longer than the sequence length of\n input_tensor, but cannot be shorter.\n dropout_prob: float. Dropout probability applied to the final output tensor.\n\n Returns:\n float tensor with same shape as `input_tensor`.\n\n Raises:\n ValueError: One of the tensor shapes or input values is invalid.\n \"\"\"\n input_shape = get_shape_list(input_tensor, expected_rank=3)\n batch_size = input_shape[0]\n seq_length = input_shape[1]\n width = input_shape[2]\n\n output = input_tensor\n\n if use_token_type:\n if token_type_ids is None:\n raise ValueError(\"`token_type_ids` must be specified if\"\n \"`use_token_type` is True.\")\n token_type_table = tf.get_variable(\n name=token_type_embedding_name,\n shape=[token_type_vocab_size, width],\n initializer=create_initializer(initializer_range))\n # This vocab will be small so we always do one-hot here, since it is always\n # faster for a small vocabulary.\n flat_token_type_ids = tf.reshape(token_type_ids, [-1])\n one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)\n token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)\n token_type_embeddings = tf.reshape(token_type_embeddings,\n [batch_size, seq_length, width])\n output += token_type_embeddings\n\n if use_position_embeddings:\n assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)\n with tf.control_dependencies([assert_op]):\n full_position_embeddings = tf.get_variable(\n name=position_embedding_name,\n shape=[max_position_embeddings, width],\n initializer=create_initializer(initializer_range))\n # Since the position embedding table is a learned variable, we create it\n # using a (long) sequence length `max_position_embeddings`. The actual\n # sequence length might be shorter than this, for faster training of\n # tasks that do not have long sequences.\n #\n # So `full_position_embeddings` is effectively an embedding table\n # for position [0, 1, 2, ..., max_position_embeddings-1], and the current\n # sequence has positions [0, 1, 2, ... seq_length-1], so we can just\n # perform a slice.\n position_embeddings = tf.slice(full_position_embeddings, [0, 0],\n [seq_length, -1])\n num_dims = len(output.shape.as_list())\n\n # Only the last two dimensions are relevant (`seq_length` and `width`), so\n # we broadcast among the first dimensions, which is typically just\n # the batch size.\n position_broadcast_shape = []\n for _ in range(num_dims - 2):\n position_broadcast_shape.append(1)\n position_broadcast_shape.extend([seq_length, width])\n position_embeddings = tf.reshape(position_embeddings,\n position_broadcast_shape)\n output += position_embeddings\n\n output = layer_norm_and_dropout(output, dropout_prob)\n return output\n\n\ndef create_attention_mask_from_input_mask(from_tensor, to_mask):\n \"\"\"Create 3D attention mask from a 2D tensor mask.\n\n Args:\n from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].\n to_mask: int32 Tensor of shape [batch_size, to_seq_length].\n\n Returns:\n float Tensor of shape [batch_size, from_seq_length, to_seq_length].\n \"\"\"\n from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])\n batch_size = from_shape[0]\n from_seq_length = from_shape[1]\n\n to_shape = get_shape_list(to_mask, expected_rank=2)\n to_seq_length = to_shape[1]\n\n to_mask = tf.cast(\n tf.reshape(to_mask, [batch_size, 1, to_seq_length]), tf.float32)\n\n # We don't assume that `from_tensor` is a mask (although it could be). We\n # don't actually care if we attend *from* padding tokens (only *to* padding)\n # tokens so we create a tensor of all ones.\n #\n # `broadcast_ones` = [batch_size, from_seq_length, 1]\n broadcast_ones = tf.ones(\n shape=[batch_size, from_seq_length, 1], dtype=tf.float32)\n\n # Here we broadcast along two dimensions to create the mask.\n mask = broadcast_ones * to_mask\n\n return mask\n\n\ndef attention_layer(from_tensor,\n to_tensor,\n attention_mask=None,\n num_attention_heads=1,\n size_per_head=512,\n query_act=None,\n key_act=None,\n value_act=None,\n attention_probs_dropout_prob=0.0,\n initializer_range=0.02,\n do_return_2d_tensor=False,\n batch_size=None,\n from_seq_length=None,\n to_seq_length=None):\n \"\"\"Performs multi-headed attention from `from_tensor` to `to_tensor`.\n\n This is an implementation of multi-headed attention based on \"Attention\n is all you Need\". If `from_tensor` and `to_tensor` are the same, then\n this is self-attention. Each timestep in `from_tensor` attends to the\n corresponding sequence in `to_tensor`, and returns a fixed-with vector.\n\n This function first projects `from_tensor` into a \"query\" tensor and\n `to_tensor` into \"key\" and \"value\" tensors. These are (effectively) a list\n of tensors of length `num_attention_heads`, where each tensor is of shape\n [batch_size, seq_length, size_per_head].\n\n Then, the query and key tensors are dot-producted and scaled. These are\n softmaxed to obtain attention probabilities. The value tensors are then\n interpolated by these probabilities, then concatenated back to a single\n tensor and returned.\n\n In practice, the multi-headed attention are done with transposes and\n reshapes rather than actual separate tensors.\n\n Args:\n from_tensor: float Tensor of shape [batch_size, from_seq_length,\n from_width].\n to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].\n attention_mask: (optional) int32 Tensor of shape [batch_size,\n from_seq_length, to_seq_length]. The values should be 1 or 0. The\n attention scores will effectively be set to -infinity for any positions in\n the mask that are 0, and will be unchanged for positions that are 1.\n num_attention_heads: int. Number of attention heads.\n size_per_head: int. Size of each attention head.\n query_act: (optional) Activation function for the query transform.\n key_act: (optional) Activation function for the key transform.\n value_act: (optional) Activation function for the value transform.\n attention_probs_dropout_prob: (optional) float. Dropout probability of the\n attention probabilities.\n initializer_range: float. Range of the weight initializer.\n do_return_2d_tensor: bool. If True, the output will be of shape [batch_size\n * from_seq_length, num_attention_heads * size_per_head]. If False, the\n output will be of shape [batch_size, from_seq_length, num_attention_heads\n * size_per_head].\n batch_size: (Optional) int. If the input is 2D, this might be the batch size\n of the 3D version of the `from_tensor` and `to_tensor`.\n from_seq_length: (Optional) If the input is 2D, this might be the seq length\n of the 3D version of the `from_tensor`.\n to_seq_length: (Optional) If the input is 2D, this might be the seq length\n of the 3D version of the `to_tensor`.\n\n Returns:\n float Tensor of shape [batch_size, from_seq_length,\n num_attention_heads * size_per_head]. (If `do_return_2d_tensor` is\n true, this will be of shape [batch_size * from_seq_length,\n num_attention_heads * size_per_head]).\n\n Raises:\n ValueError: Any of the arguments or tensor shapes are invalid.\n \"\"\"\n\n def transpose_for_scores(input_tensor, batch_size, num_attention_heads,\n seq_length, width):\n output_tensor = tf.reshape(\n input_tensor, [batch_size, seq_length, num_attention_heads, width])\n\n output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])\n return output_tensor\n\n from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])\n to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])\n\n if len(from_shape) != len(to_shape):\n raise ValueError(\n \"The rank of `from_tensor` must match the rank of `to_tensor`.\")\n\n if len(from_shape) == 3:\n batch_size = from_shape[0]\n from_seq_length = from_shape[1]\n to_seq_length = to_shape[1]\n elif len(from_shape) == 2:\n if (batch_size is None or from_seq_length is None or to_seq_length is None):\n raise ValueError(\n \"When passing in rank 2 tensors to attention_layer, the values \"\n \"for `batch_size`, `from_seq_length`, and `to_seq_length` \"\n \"must all be specified.\")\n\n # Scalar dimensions referenced here:\n # B = batch size (number of sequences)\n # F = `from_tensor` sequence length\n # T = `to_tensor` sequence length\n # N = `num_attention_heads`\n # H = `size_per_head`\n\n from_tensor_2d = reshape_to_matrix(from_tensor)\n to_tensor_2d = reshape_to_matrix(to_tensor)\n\n # `query_layer` = [B*F, N*H]\n query_layer = tf.layers.dense(\n from_tensor_2d,\n num_attention_heads * size_per_head,\n activation=query_act,\n name=\"query\",\n kernel_initializer=create_initializer(initializer_range))\n\n # `key_layer` = [B*T, N*H]\n key_layer = tf.layers.dense(\n to_tensor_2d,\n num_attention_heads * size_per_head,\n activation=key_act,\n name=\"key\",\n kernel_initializer=create_initializer(initializer_range))\n\n # `value_layer` = [B*T, N*H]\n value_layer = tf.layers.dense(\n to_tensor_2d,\n num_attention_heads * size_per_head,\n activation=value_act,\n name=\"value\",\n kernel_initializer=create_initializer(initializer_range))\n\n # `query_layer` = [B, N, F, H]\n query_layer = transpose_for_scores(query_layer, batch_size,\n num_attention_heads, from_seq_length,\n size_per_head)\n\n # `key_layer` = [B, N, T, H]\n key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads,\n to_seq_length, size_per_head)\n\n # Take the dot product between \"query\" and \"key\" to get the raw\n # attention scores.\n # `attention_scores` = [B, N, F, T]\n attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)\n attention_scores = tf.multiply(attention_scores,\n 1.0 / math.sqrt(float(size_per_head)))\n\n if attention_mask is not None:\n # `attention_mask` = [B, 1, F, T]\n attention_mask = tf.expand_dims(attention_mask, axis=[1])\n\n # Since attention_mask is 1.0 for positions we want to attend and 0.0 for\n # masked positions, this operation will create a tensor which is 0.0 for\n # positions we want to attend and -10000.0 for masked positions.\n adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0\n\n # Since we are adding it to the raw scores before the softmax, this is\n # effectively the same as removing these entirely.\n attention_scores += adder\n\n # Normalize the attention scores to probabilities.\n # `attention_probs` = [B, N, F, T]\n attention_probs = tf.nn.softmax(attention_scores)\n\n # This is actually dropping out entire tokens to attend to, which might\n # seem a bit unusual, but is taken from the original Transformer paper.\n attention_probs = dropout(attention_probs, attention_probs_dropout_prob)\n\n # `value_layer` = [B, T, N, H]\n value_layer = tf.reshape(\n value_layer,\n [batch_size, to_seq_length, num_attention_heads, size_per_head])\n\n # `value_layer` = [B, N, T, H]\n value_layer = tf.transpose(value_layer, [0, 2, 1, 3])\n\n # `context_layer` = [B, N, F, H]\n context_layer = tf.matmul(attention_probs, value_layer)\n\n # `context_layer` = [B, F, N, H]\n context_layer = tf.transpose(context_layer, [0, 2, 1, 3])\n\n if do_return_2d_tensor:\n # `context_layer` = [B*F, N*V]\n context_layer = tf.reshape(\n context_layer,\n [batch_size * from_seq_length, num_attention_heads * size_per_head])\n else:\n # `context_layer` = [B, F, N*V]\n context_layer = tf.reshape(\n context_layer,\n [batch_size, from_seq_length, num_attention_heads * size_per_head])\n\n return context_layer\n\n\ndef transformer_model(input_tensor,\n attention_mask=None,\n hidden_size=768,\n num_hidden_layers=12,\n num_attention_heads=12,\n intermediate_size=3072,\n intermediate_act_fn=gelu,\n hidden_dropout_prob=0.1,\n attention_probs_dropout_prob=0.1,\n initializer_range=0.02,\n do_return_all_layers=False):\n \"\"\"Multi-headed, multi-layer Transformer from \"Attention is All You Need\".\n\n This is almost an exact implementation of the original Transformer encoder.\n\n See the original paper:\n https://arxiv.org/abs/1706.03762\n\n Also see:\n https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py\n\n Args:\n input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].\n attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,\n seq_length], with 1 for positions that can be attended to and 0 in\n positions that should not be.\n hidden_size: int. Hidden size of the Transformer.\n num_hidden_layers: int. Number of layers (blocks) in the Transformer.\n num_attention_heads: int. Number of attention heads in the Transformer.\n intermediate_size: int. The size of the \"intermediate\" (a.k.a., feed\n forward) layer.\n intermediate_act_fn: function. The non-linear activation function to apply\n to the output of the intermediate/feed-forward layer.\n hidden_dropout_prob: float. Dropout probability for the hidden layers.\n attention_probs_dropout_prob: float. Dropout probability of the attention\n probabilities.\n initializer_range: float. Range of the initializer (stddev of truncated\n normal).\n do_return_all_layers: Whether to also return all layers or just the final\n layer.\n\n Returns:\n float Tensor of shape [batch_size, seq_length, hidden_size], the final\n hidden layer of the Transformer.\n\n Raises:\n ValueError: A Tensor shape or parameter is invalid.\n \"\"\"\n if hidden_size % num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (hidden_size, num_attention_heads))\n\n attention_head_size = int(hidden_size / num_attention_heads)\n input_shape = get_shape_list(input_tensor, expected_rank=3)\n batch_size = input_shape[0]\n seq_length = input_shape[1]\n input_width = input_shape[2]\n\n # The Transformer performs sum residuals on all layers so the input needs\n # to be the same as the hidden size.\n if input_width != hidden_size:\n raise ValueError(\"The width of the input tensor (%d) != hidden size (%d)\" %\n (input_width, hidden_size))\n\n # We keep the representation as a 2D tensor to avoid re-shaping it back and\n # forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on\n # the GPU/CPU but may not be free on the TPU, so we want to minimize them to\n # help the optimizer.\n prev_output = reshape_to_matrix(input_tensor)\n\n all_layer_outputs = []\n for layer_idx in range(num_hidden_layers):\n with tf.variable_scope(\"layer_%d\" % layer_idx):\n layer_input = prev_output\n\n with tf.variable_scope(\"attention\"):\n attention_heads = []\n with tf.variable_scope(\"self\"):\n attention_head = attention_layer(\n from_tensor=layer_input,\n to_tensor=layer_input,\n attention_mask=attention_mask,\n num_attention_heads=num_attention_heads,\n size_per_head=attention_head_size,\n attention_probs_dropout_prob=attention_probs_dropout_prob,\n initializer_range=initializer_range,\n do_return_2d_tensor=True,\n batch_size=batch_size,\n from_seq_length=seq_length,\n to_seq_length=seq_length)\n attention_heads.append(attention_head)\n\n attention_output = None\n if len(attention_heads) == 1:\n attention_output = attention_heads[0]\n else:\n # In the case where we have other sequences, we just concatenate\n # them to the self-attention head before the projection.\n attention_output = tf.concat(attention_heads, axis=-1)\n\n # Run a linear projection of `hidden_size` then add a residual\n # with `layer_input`.\n with tf.variable_scope(\"output\"):\n attention_output = tf.layers.dense(\n attention_output,\n hidden_size,\n kernel_initializer=create_initializer(initializer_range))\n attention_output = dropout(attention_output, hidden_dropout_prob)\n attention_output = layer_norm(attention_output + layer_input)\n\n # The activation is only applied to the \"intermediate\" hidden layer.\n with tf.variable_scope(\"intermediate\"):\n intermediate_output = tf.layers.dense(\n attention_output,\n intermediate_size,\n activation=intermediate_act_fn,\n kernel_initializer=create_initializer(initializer_range))\n\n # Down-project back to `hidden_size` then add the residual.\n with tf.variable_scope(\"output\"):\n layer_output = tf.layers.dense(\n intermediate_output,\n hidden_size,\n kernel_initializer=create_initializer(initializer_range))\n layer_output = dropout(layer_output, hidden_dropout_prob)\n layer_output = layer_norm(layer_output + attention_output)\n prev_output = layer_output\n all_layer_outputs.append(layer_output)\n\n if do_return_all_layers:\n final_outputs = []\n for layer_output in all_layer_outputs:\n final_output = reshape_from_matrix(layer_output, input_shape)\n final_outputs.append(final_output)\n return final_outputs\n else:\n final_output = reshape_from_matrix(prev_output, input_shape)\n return final_output\n\n\ndef get_shape_list(tensor, expected_rank=None, name=None):\n \"\"\"Returns a list of the shape of tensor, preferring static dimensions.\n\n Args:\n tensor: A tf.Tensor object to find the shape of.\n expected_rank: (optional) int. The expected rank of `tensor`. If this is\n specified and the `tensor` has a different rank, and exception will be\n thrown.\n name: Optional name of the tensor for the error message.\n\n Returns:\n A list of dimensions of the shape of tensor. All static dimensions will\n be returned as python integers, and dynamic dimensions will be returned\n as tf.Tensor scalars.\n \"\"\"\n if name is None:\n name = tensor.name\n\n if expected_rank is not None:\n assert_rank(tensor, expected_rank, name)\n\n shape = tensor.shape.as_list()\n\n non_static_indexes = []\n for (index, dim) in enumerate(shape):\n if dim is None:\n non_static_indexes.append(index)\n\n if not non_static_indexes:\n return shape\n\n dyn_shape = tf.shape(tensor)\n for index in non_static_indexes:\n shape[index] = dyn_shape[index]\n return shape\n\n\ndef reshape_to_matrix(input_tensor):\n \"\"\"Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix).\"\"\"\n ndims = input_tensor.shape.ndims\n if ndims < 2:\n raise ValueError(\"Input tensor must have at least rank 2. Shape = %s\" %\n (input_tensor.shape))\n if ndims == 2:\n return input_tensor\n\n width = input_tensor.shape[-1]\n output_tensor = tf.reshape(input_tensor, [-1, width])\n return output_tensor\n\n\ndef reshape_from_matrix(output_tensor, orig_shape_list):\n \"\"\"Reshapes a rank 2 tensor back to its original rank >= 2 tensor.\"\"\"\n if len(orig_shape_list) == 2:\n return output_tensor\n\n output_shape = get_shape_list(output_tensor)\n\n orig_dims = orig_shape_list[0:-1]\n width = output_shape[-1]\n\n return tf.reshape(output_tensor, orig_dims + [width])\n\n\ndef assert_rank(tensor, expected_rank, name=None):\n \"\"\"Raises an exception if the tensor rank is not of the expected rank.\n\n Args:\n tensor: A tf.Tensor to check the rank of.\n expected_rank: Python integer or list of integers, expected rank.\n name: Optional name of the tensor for the error message.\n\n Raises:\n ValueError: If the expected shape doesn't match the actual shape.\n \"\"\"\n if name is None:\n name = tensor.name\n\n expected_rank_dict = {}\n if isinstance(expected_rank, six.integer_types):\n expected_rank_dict[expected_rank] = True\n else:\n for x in expected_rank:\n expected_rank_dict[x] = True\n\n actual_rank = tensor.shape.ndims\n if actual_rank not in expected_rank_dict:\n scope_name = tf.get_variable_scope().name\n raise ValueError(\n \"For the tensor `%s` in scope `%s`, the actual rank \"\n \"`%d` (shape = %s) is not equal to the expected rank `%s`\" %\n (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))\n"
},
{
"path": "bert/optimization.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Functions and classes related to optimization (weight updates).\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport re\nimport tensorflow as tf\n\n\ndef create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu):\n \"\"\"Creates an optimizer training op.\"\"\"\n global_step = tf.train.get_or_create_global_step()\n\n learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)\n\n # Implements linear decay of the learning rate.\n learning_rate = tf.train.polynomial_decay(\n learning_rate,\n global_step,\n num_train_steps,\n end_learning_rate=0.0,\n power=1.0,\n cycle=False)\n\n # Implements linear warmup. I.e., if global_step < num_warmup_steps, the\n # learning rate will be `global_step/num_warmup_steps * init_lr`.\n if num_warmup_steps:\n global_steps_int = tf.cast(global_step, tf.int32)\n warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)\n\n global_steps_float = tf.cast(global_steps_int, tf.float32)\n warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)\n\n warmup_percent_done = global_steps_float / warmup_steps_float\n warmup_learning_rate = init_lr * warmup_percent_done\n\n is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)\n learning_rate = (\n (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)\n\n # It is recommended that you use this optimizer for fine tuning, since this\n # is how the model was trained (note that the Adam m/v variables are NOT\n # loaded from init_checkpoint.)\n optimizer = AdamWeightDecayOptimizer(\n learning_rate=learning_rate,\n weight_decay_rate=0.01,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=1e-6,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"])\n\n if use_tpu:\n optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)\n\n tvars = tf.trainable_variables()\n grads = tf.gradients(loss, tvars)\n\n # This is how the model was pre-trained.\n (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)\n\n train_op = optimizer.apply_gradients(\n zip(grads, tvars), global_step=global_step)\n\n # Normally the global step update is done inside of `apply_gradients`.\n # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use\n # a different optimizer, you should probably take this line out.\n new_global_step = global_step + 1\n train_op = tf.group(train_op, [global_step.assign(new_global_step)])\n return train_op\n\n\nclass AdamWeightDecayOptimizer(tf.train.Optimizer):\n \"\"\"A basic Adam optimizer that includes \"correct\" L2 weight decay.\"\"\"\n\n def __init__(self,\n learning_rate,\n weight_decay_rate=0.0,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=1e-6,\n exclude_from_weight_decay=None,\n name=\"AdamWeightDecayOptimizer\"):\n \"\"\"Constructs a AdamWeightDecayOptimizer.\"\"\"\n super(AdamWeightDecayOptimizer, self).__init__(False, name)\n\n self.learning_rate = learning_rate\n self.weight_decay_rate = weight_decay_rate\n self.beta_1 = beta_1\n self.beta_2 = beta_2\n self.epsilon = epsilon\n self.exclude_from_weight_decay = exclude_from_weight_decay\n\n def apply_gradients(self, grads_and_vars, global_step=None, name=None):\n \"\"\"See base class.\"\"\"\n assignments = []\n for (grad, param) in grads_and_vars:\n if grad is None or param is None:\n continue\n\n param_name = self._get_variable_name(param.name)\n\n m = tf.get_variable(\n name=param_name + \"/adam_m\",\n shape=param.shape.as_list(),\n dtype=tf.float32,\n trainable=False,\n initializer=tf.zeros_initializer())\n v = tf.get_variable(\n name=param_name + \"/adam_v\",\n shape=param.shape.as_list(),\n dtype=tf.float32,\n trainable=False,\n initializer=tf.zeros_initializer())\n\n # Standard Adam update.\n next_m = (\n tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))\n next_v = (\n tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,\n tf.square(grad)))\n\n update = next_m / (tf.sqrt(next_v) + self.epsilon)\n\n # Just adding the square of the weights to the loss function is *not*\n # the correct way of using L2 regularization/weight decay with Adam,\n # since that will interact with the m and v parameters in strange ways.\n #\n # Instead we want ot decay the weights in a manner that doesn't interact\n # with the m/v parameters. This is equivalent to adding the square\n # of the weights to the loss with plain (non-momentum) SGD.\n if self._do_use_weight_decay(param_name):\n update += self.weight_decay_rate * param\n\n update_with_lr = self.learning_rate * update\n\n next_param = param - update_with_lr\n\n assignments.extend(\n [param.assign(next_param),\n m.assign(next_m),\n v.assign(next_v)])\n return tf.group(*assignments, name=name)\n\n def _do_use_weight_decay(self, param_name):\n \"\"\"Whether to use L2 weight decay for `param_name`.\"\"\"\n if not self.weight_decay_rate:\n return False\n if self.exclude_from_weight_decay:\n for r in self.exclude_from_weight_decay:\n if re.search(r, param_name) is not None:\n return False\n return True\n\n def _get_variable_name(self, param_name):\n \"\"\"Get the variable name from the tensor name.\"\"\"\n m = re.match(\"^(.*):\\\\d+$\", param_name)\n if m is not None:\n param_name = m.group(1)\n return param_name\n"
},
{
"path": "bert/tokenization.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tokenization classes.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport re\nimport unicodedata\nimport six\nimport tensorflow as tf\n\n\ndef validate_case_matches_checkpoint(do_lower_case, init_checkpoint):\n \"\"\"Checks whether the casing config is consistent with the checkpoint name.\"\"\"\n\n # The casing has to be passed in by the user and there is no explicit check\n # as to whether it matches the checkpoint. The casing information probably\n # should have been stored in the bert_config.json file, but it's not, so\n # we have to heuristically detect it to validate.\n\n if not init_checkpoint:\n return\n\n m = re.match(\"^.*?([A-Za-z0-9_-]+)/bert_model.ckpt\", init_checkpoint)\n if m is None:\n return\n\n model_name = m.group(1)\n\n lower_models = [\n \"uncased_L-24_H-1024_A-16\", \"uncased_L-12_H-768_A-12\",\n \"multilingual_L-12_H-768_A-12\", \"chinese_L-12_H-768_A-12\"\n ]\n\n cased_models = [\n \"cased_L-12_H-768_A-12\", \"cased_L-24_H-1024_A-16\",\n \"multi_cased_L-12_H-768_A-12\"\n ]\n\n is_bad_config = False\n if model_name in lower_models and not do_lower_case:\n is_bad_config = True\n actual_flag = \"False\"\n case_name = \"lowercased\"\n opposite_flag = \"True\"\n\n if model_name in cased_models and do_lower_case:\n is_bad_config = True\n actual_flag = \"True\"\n case_name = \"cased\"\n opposite_flag = \"False\"\n\n if is_bad_config:\n raise ValueError(\n \"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. \"\n \"However, `%s` seems to be a %s model, so you \"\n \"should pass in `--do_lower_case=%s` so that the fine-tuning matches \"\n \"how the model was pre-training. If this error is wrong, please \"\n \"just comment out this check.\" % (actual_flag, init_checkpoint,\n model_name, case_name, opposite_flag))\n\n\ndef convert_to_unicode(text):\n \"\"\"Converts `text` to Unicode (if it's not already), assuming utf-8 input.\"\"\"\n if six.PY3:\n if isinstance(text, str):\n return text\n elif isinstance(text, bytes):\n return text.decode(\"utf-8\", \"ignore\")\n else:\n raise ValueError(\"Unsupported string type: %s\" % (type(text)))\n elif six.PY2:\n if isinstance(text, str):\n return text.decode(\"utf-8\", \"ignore\")\n elif isinstance(text, unicode):\n return text\n else:\n raise ValueError(\"Unsupported string type: %s\" % (type(text)))\n else:\n raise ValueError(\"Not running on Python2 or Python 3?\")\n\n\ndef printable_text(text):\n \"\"\"Returns text encoded in a way suitable for print or `tf.logging`.\"\"\"\n\n # These functions want `str` for both Python2 and Python3, but in one case\n # it's a Unicode string and in the other it's a byte string.\n if six.PY3:\n if isinstance(text, str):\n return text\n elif isinstance(text, bytes):\n return text.decode(\"utf-8\", \"ignore\")\n else:\n raise ValueError(\"Unsupported string type: %s\" % (type(text)))\n elif six.PY2:\n if isinstance(text, str):\n return text\n elif isinstance(text, unicode):\n return text.encode(\"utf-8\")\n else:\n raise ValueError(\"Unsupported string type: %s\" % (type(text)))\n else:\n raise ValueError(\"Not running on Python2 or Python 3?\")\n\n\ndef load_vocab(vocab_file):\n \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n vocab = collections.OrderedDict()\n index = 0\n with tf.gfile.GFile(vocab_file, \"r\") as reader:\n while True:\n token = convert_to_unicode(reader.readline())\n if not token:\n break\n token = token.strip()\n vocab[token] = index\n index += 1\n return vocab\n\n\ndef convert_by_vocab(vocab, items):\n \"\"\"Converts a sequence of [tokens|ids] using the vocab.\"\"\"\n output = []\n for item in items:\n output.append(vocab[item])\n return output\n\n\ndef convert_tokens_to_ids(vocab, tokens):\n return convert_by_vocab(vocab, tokens)\n\n\ndef convert_ids_to_tokens(inv_vocab, ids):\n return convert_by_vocab(inv_vocab, ids)\n\n\ndef whitespace_tokenize(text):\n \"\"\"Runs basic whitespace cleaning and splitting on a piece of text.\"\"\"\n text = text.strip()\n if not text:\n return []\n tokens = text.split()\n return tokens\n\n\nclass FullTokenizer(object):\n \"\"\"Runs end-to-end tokenziation.\"\"\"\n\n def __init__(self, vocab_file, do_lower_case=True):\n self.vocab = load_vocab(vocab_file)\n self.inv_vocab = {v: k for k, v in self.vocab.items()}\n self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\n self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)\n\n def tokenize(self, text):\n split_tokens = []\n for token in self.basic_tokenizer.tokenize(text):\n for sub_token in self.wordpiece_tokenizer.tokenize(token):\n split_tokens.append(sub_token)\n\n return split_tokens\n\n def convert_tokens_to_ids(self, tokens):\n return convert_by_vocab(self.vocab, tokens)\n\n def convert_ids_to_tokens(self, ids):\n return convert_by_vocab(self.inv_vocab, ids)\n\n\nclass BasicTokenizer(object):\n \"\"\"Runs basic tokenization (punctuation splitting, lower casing, etc.).\"\"\"\n\n def __init__(self, do_lower_case=True):\n \"\"\"Constructs a BasicTokenizer.\n\n Args:\n do_lower_case: Whether to lower case the input.\n \"\"\"\n self.do_lower_case = do_lower_case\n\n def tokenize(self, text):\n \"\"\"Tokenizes a piece of text.\"\"\"\n text = convert_to_unicode(text)\n text = self._clean_text(text)\n\n # This was added on November 1st, 2018 for the multilingual and Chinese\n # models. This is also applied to the English models now, but it doesn't\n # matter since the English models were not trained on any Chinese data\n # and generally don't have any Chinese data in them (there are Chinese\n # characters in the vocabulary because Wikipedia does have some Chinese\n # words in the English Wikipedia.).\n text = self._tokenize_chinese_chars(text)\n\n orig_tokens = whitespace_tokenize(text)\n split_tokens = []\n for token in orig_tokens:\n if self.do_lower_case:\n token = token.lower()\n token = self._run_strip_accents(token)\n split_tokens.extend(self._run_split_on_punc(token))\n\n output_tokens = whitespace_tokenize(\" \".join(split_tokens))\n return output_tokens\n\n def _run_strip_accents(self, text):\n \"\"\"Strips accents from a piece of text.\"\"\"\n text = unicodedata.normalize(\"NFD\", text)\n output = []\n for char in text:\n cat = unicodedata.category(char)\n if cat == \"Mn\":\n continue\n output.append(char)\n return \"\".join(output)\n\n def _run_split_on_punc(self, text):\n \"\"\"Splits punctuation on a piece of text.\"\"\"\n chars = list(text)\n i = 0\n start_new_word = True\n output = []\n while i < len(chars):\n char = chars[i]\n if _is_punctuation(char):\n output.append([char])\n start_new_word = True\n else:\n if start_new_word:\n output.append([])\n start_new_word = False\n output[-1].append(char)\n i += 1\n\n return [\"\".join(x) for x in output]\n\n def _tokenize_chinese_chars(self, text):\n \"\"\"Adds whitespace around any CJK character.\"\"\"\n output = []\n for char in text:\n cp = ord(char)\n if self._is_chinese_char(cp):\n output.append(\" \")\n output.append(char)\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)\n\n def _is_chinese_char(self, cp):\n \"\"\"Checks whether CP is the codepoint of a CJK character.\"\"\"\n # This defines a \"chinese character\" as anything in the CJK Unicode block:\n # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)\n #\n # Note that the CJK Unicode block is NOT all Japanese and Korean characters,\n # despite its name. The modern Korean Hangul alphabet is a different block,\n # as is Japanese Hiragana and Katakana. Those alphabets are used to write\n # space-separated words, so they are not treated specially and handled\n # like the all of the other languages.\n if ((cp >= 0x4E00 and cp <= 0x9FFF) or #\n (cp >= 0x3400 and cp <= 0x4DBF) or #\n (cp >= 0x20000 and cp <= 0x2A6DF) or #\n (cp >= 0x2A700 and cp <= 0x2B73F) or #\n (cp >= 0x2B740 and cp <= 0x2B81F) or #\n (cp >= 0x2B820 and cp <= 0x2CEAF) or\n (cp >= 0xF900 and cp <= 0xFAFF) or #\n (cp >= 0x2F800 and cp <= 0x2FA1F)): #\n return True\n\n return False\n\n def _clean_text(self, text):\n \"\"\"Performs invalid character removal and whitespace cleanup on text.\"\"\"\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)\n\n\nclass WordpieceTokenizer(object):\n \"\"\"Runs WordPiece tokenziation.\"\"\"\n\n def __init__(self, vocab, unk_token=\"[UNK]\", max_input_chars_per_word=200):\n self.vocab = vocab\n self.unk_token = unk_token\n self.max_input_chars_per_word = max_input_chars_per_word\n\n def tokenize(self, text):\n \"\"\"Tokenizes a piece of text into its word pieces.\n\n This uses a greedy longest-match-first algorithm to perform tokenization\n using the given vocabulary.\n\n For example:\n input = \"unaffable\"\n output = [\"un\", \"##aff\", \"##able\"]\n\n Args:\n text: A single token or whitespace separated tokens. This should have\n already been passed through `BasicTokenizer.\n\n Returns:\n A list of wordpiece tokens.\n \"\"\"\n\n text = convert_to_unicode(text)\n\n output_tokens = []\n for token in whitespace_tokenize(text):\n chars = list(token)\n if len(chars) > self.max_input_chars_per_word:\n output_tokens.append(self.unk_token)\n continue\n\n is_bad = False\n start = 0\n sub_tokens = []\n while start < len(chars):\n end = len(chars)\n cur_substr = None\n while start < end:\n substr = \"\".join(chars[start:end])\n if start > 0:\n substr = \"##\" + substr\n if substr in self.vocab:\n cur_substr = substr\n break\n end -= 1\n if cur_substr is None:\n is_bad = True\n break\n sub_tokens.append(cur_substr)\n start = end\n\n if is_bad:\n output_tokens.append(self.unk_token)\n else:\n output_tokens.extend(sub_tokens)\n return output_tokens\n\n\ndef _is_whitespace(char):\n \"\"\"Checks whether `chars` is a whitespace character.\"\"\"\n # \\t, \\n, and \\r are technically contorl characters but we treat them\n # as whitespace since they are generally considered as such.\n if char == \" \" or char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n return True\n cat = unicodedata.category(char)\n if cat == \"Zs\":\n return True\n return False\n\n\ndef _is_control(char):\n \"\"\"Checks whether `chars` is a control character.\"\"\"\n # These are technically control characters but we count them as whitespace\n # characters.\n if char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n return False\n cat = unicodedata.category(char)\n if cat.startswith(\"C\"):\n return True\n return False\n\n\ndef _is_punctuation(char):\n \"\"\"Checks whether `chars` is a punctuation character.\"\"\"\n cp = ord(char)\n # We treat all non-letter/number ASCII as punctuation.\n # Characters such as \"^\", \"$\", and \"`\" are not in the Unicode\n # Punctuation class but we treat them as punctuation anyways, for\n # consistency.\n if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or\n (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):\n return True\n cat = unicodedata.category(char)\n if cat.startswith(\"P\"):\n return True\n return False\n"
},
{
"path": "conll-2012/scorer/v8.01/README.txt",
"content": "NAME\n CorScorer: Perl package for scoring coreference resolution systems\n using different metrics.\n\n\nVERSION\n v8.01 -- reference implementations of MUC, B-cubed, CEAF and BLANC metrics.\n\n\nCHANGES SINCE v8.0\n - fixed a bug that crashed the BLANC scorer when a duplicate singleton\n mention was present in the response.\n\nINSTALLATION\n Requirements:\n 1. Perl: downloadable from http://perl.org\n 2. Algorithm-Munkres: included in this package and downloadable\n from CPAN http://search.cpan.org/~tpederse/Algorithm-Munkres-0.08\n\nUSE\n This package is distributed with two scripts to execute the scorer from\n the command line.\n\n Windows (tm): scorer.bat\n Linux: scorer.pl\n\n\nSYNOPSIS\n use CorScorer;\n\n $metric = 'ceafm';\n\n # Scores the whole dataset\n &CorScorer::Score($metric, $keys_file, $response_file);\n\n # Scores one file\n &CorScorer::Score($metric, $keys_file, $response_file, $name);\n\n\nINPUT\n metric: the metric desired to score the results:\n muc: MUCScorer (Vilain et al, 1995)\n bcub: B-Cubed (Bagga and Baldwin, 1998)\n ceafm: CEAF (Luo et al., 2005) using mention-based similarity\n ceafe: CEAF (Luo et al., 2005) using entity-based similarity\n blanc: BLANC (Luo et al., 2014) BLANC metric for gold and predicted mentions\n all: uses all the metrics to score\n\n keys_file: file with expected coreference chains in CoNLL-2011/2012 format\n\n response_file: file with output of coreference system (CoNLL-2011/2012 format)\n\n name: [optional] the name of the document to score. If name is not\n given, all the documents in the dataset will be scored. If given\n name is \"none\" then all the documents are scored but only total\n results are shown.\n\n\nOUTPUT\n The score subroutine returns an array with four values in this order:\n 1) Recall numerator\n 2) Recall denominator\n 3) Precision numerator\n 4) Precision denominator\n\n Also recall, precision and F1 are printed in the standard output when variable\n $VERBOSE is not null.\n\n Final scores:\n Recall = recall_numerator / recall_denominator\n Precision = precision_numerator / precision_denominator\n F1 = 2 * Recall * Precision / (Recall + Precision)\n\n Identification of mentions\n An scorer for identification of mentions (recall, precision and F1) is also included.\n Mentions from system response are compared with key mentions. This version performs\n strict mention matching as was used in the CoNLL-2011 and 2012 shared tasks.\n\nAUTHORS\n Emili Sapena, Universitat Politècnica de Catalunya, http://www.lsi.upc.edu/~esapena, esapena lsi.upc.edu\n Sameer Pradhan, sameer.pradhan childrens.harvard.edu\n Sebastian Martschat, sebastian.martschat h-its.org\n Xiaoqiang Luo, xql google.com\n\nCOPYRIGHT AND LICENSE\n Copyright (C) 2009-2011, Emili Sapena esapena lsi.upc.edu\n 2011-2014, Sameer Pradhan sameer.pradhan childrens.harvard.edu\n\n This program is free software; you can redistribute it and/or modify it\n under the terms of the GNU General Public License as published by the\n Free Software Foundation; either version 2 of the License, or (at your\n option) any later version. This program is distributed in the hope that\n it will be useful, but WITHOUT ANY WARRANTY; without even the implied\n warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License along\n with this program; if not, write to the Free Software Foundation, Inc.,\n 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.\n\n"
},
{
"path": "conll-2012/scorer/v8.01/scorer.bat",
"content": "@rem = '--*-Perl-*--\n@echo off\nif \"%OS%\" == \"Windows_NT\" goto WinNT\nperl -x -S \"%0\" %1 %2 %3 %4 %5 %6 %7 %8 %9\ngoto endofperl\n:WinNT\nperl -x -S %0 %*\nif NOT \"%COMSPEC%\" == \"%SystemRoot%\\system32\\cmd.exe\" goto endofperl\nif %errorlevel% == 9009 echo You do not have Perl in your PATH.\nif errorlevel 1 goto script_failed_so_exit_with_non_zero_val 2>nul\ngoto endofperl\n@rem ';\n#!perl\n#line 15\n\nBEGIN {\n $d = $0;\n $d =~ s/\\/[^\\/][^\\/]*$//g;\n push(@INC, $d.\"/lib\");\n}\n\nuse strict;\nuse CorScorer;\n\nif (@ARGV < 3) {\n print q|\n use: scorer.bat [name]\n \n metric: the metric desired to score the results:\n muc: MUCScorer (Vilain et al, 1995)\n bcub: B-Cubed (Bagga and Baldwin, 1998)\n ceafm: CEAF (Luo et al, 2005) using mention-based similarity\n ceafe: CEAF (Luo et al, 2005) using entity-based similarity\n all: uses all the metrics to score\n \n keys_file: file with expected coreference chains in SemEval format\n \n response_file: file with output of coreference system (SemEval format)\n \n name: [optional] the name of the document to score. If name is not\n given, all the documents in the dataset will be scored. If given\n name is \"none\" then all the documents are scored but only total\n results are shown.\n \n |;\n exit;\n}\n\nmy $metric = shift (@ARGV);\nif ($metric !~ /^(muc|bcub|ceafm|ceafe|all)/i) {\n print \"Invalid metric\\n\";\n exit;\n}\n\n\nif ($metric eq 'all') {\n foreach my $m ('muc', 'bcub', 'ceafm', 'ceafe') {\n print \"\\nMETRIC $m:\\n\";\n &CorScorer::Score( $m, @ARGV );\n }\n}\nelse {\n &CorScorer::Score( $metric, @ARGV );\n}\n\n__END__\n:endofperl\n"
},
{
"path": "conll-2012/scorer/v8.01/scorer.pl",
"content": "#!/usr/bin/perl\n\nBEGIN {\n $d = $0;\n $d =~ s/\\/[^\\/][^\\/]*$//g;\n\n if ($d eq $0) {\n unshift(@INC, \"lib\");\n }\n else {\n unshift(@INC, $d . \"/lib\");\n }\n}\n\nuse strict;\nuse CorScorer;\n\nif (@ARGV < 3) {\n print q|\nuse: scorer.pl [name]\n\n metric: the metric desired to score the results:\n muc: MUCScorer (Vilain et al, 1995)\n bcub: B-Cubed (Bagga and Baldwin, 1998)\n ceafm: CEAF (Luo et al, 2005) using mention-based similarity\n ceafe: CEAF (Luo et al, 2005) using entity-based similarity\n blanc: BLANC\n all: uses all the metrics to score\n\n keys_file: file with expected coreference chains in SemEval format\n\n response_file: file with output of coreference system (SemEval format)\n\n name: [optional] the name of the document to score. If name is not\n given, all the documents in the dataset will be scored. If given\n name is \"none\" then all the documents are scored but only total\n results are shown.\n\n|;\n exit;\n}\n\nmy $metric = shift(@ARGV);\nif ($metric !~ /^(muc|bcub|ceafm|ceafe|blanc|all)/i) {\n print \"Invalid metric\\n\";\n exit;\n}\n\nif ($metric eq 'all') {\n foreach my $m ('muc', 'bcub', 'ceafm', 'ceafe', 'blanc') {\n print \"\\nMETRIC $m:\\n\";\n &CorScorer::Score($m, @ARGV);\n }\n}\nelse {\n &CorScorer::Score($metric, @ARGV);\n}\n\n"
},
{
"path": "data_utils/config_utils.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# config utils for the mention proposal and corefqa \n\n\n\nimport os \nimport json \nimport tensorflow as tf \n\n\nclass ModelConfig(object):\n def __init__(self, tf_flags, output_dir, model_sign=\"model\"):\n key_value_pairs = tf_flags.flag_values_dict()\n\n for item_key, item_value in key_value_pairs.items():\n self.__dict__[item_key] = item_value \n\n self.output_dir = output_dir \n config_path = os.path.join(self.output_dir, \"{}_config.json\".format(model_sign))\n\n def logging_configs(self):\n tf.logging.info(\"$*$\"*30)\n tf.logging.info(\"****** print model configs : ******\")\n tf.logging.info(\"$*$\"*30)\n\n for item_key, item_value in self.__dict__.items():\n tf.logging.info(\"{} : {}\".format(str(item_key), str(item_value)))\n"
},
{
"path": "data_utils/conll.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# use the offical conll-2012 evaluation scripts to evaluate the trained model and save the evaluation results into files. \n\n\nimport os \nimport re\nimport collections\nimport operator\nimport subprocess\nimport tempfile\n\n\n\nREPO_PATH = \"/\".join(os.path.realpath(__file__).split(\"/\")[:-2])\nBEGIN_DOCUMENT_REGEX = re.compile(r\"#begin document \\((.*)\\); part (\\d+)\")\nCOREF_RESULTS_REGEX = re.compile(\n r\".*Coreference: Recall: \\([0-9.]+ / [0-9.]+\\) ([0-9.]+)%\\tPrecision: \\([0-9.]+ / [0-9.]+\\) ([0-9.]+)%\\tF1: ([0-9.]+)%.*\",\n re.DOTALL)\n\n\n\ndef get_doc_key(doc_id, part):\n return \"{}_{}\".format(doc_id, int(part))\n\n\ndef output_conll(input_file, output_file, predictions, subtoken_map):\n prediction_map = {}\n for doc_key, clusters in predictions.items():\n start_map = collections.defaultdict(list)\n end_map = collections.defaultdict(list)\n word_map = collections.defaultdict(list)\n for cluster_id, mentions in enumerate(clusters):\n for start, end in mentions:\n start, end = subtoken_map[doc_key][start], subtoken_map[doc_key][end]\n if start == end:\n word_map[start].append(cluster_id)\n else:\n start_map[start].append((cluster_id, end))\n end_map[end].append((cluster_id, start))\n for k, v in start_map.items():\n start_map[k] = [cluster_id for cluster_id, end in sorted(v, key=operator.itemgetter(1), reverse=True)]\n for k, v in end_map.items():\n end_map[k] = [cluster_id for cluster_id, start in sorted(v, key=operator.itemgetter(1), reverse=True)]\n prediction_map[doc_key] = (start_map, end_map, word_map)\n\n word_index = 0\n for line in input_file.readlines():\n row = line.split()\n if len(row) == 0:\n output_file.write(\"\\n\")\n elif row[0].startswith(\"#\"):\n begin_match = re.match(BEGIN_DOCUMENT_REGEX, line)\n if begin_match:\n doc_key = get_doc_key(begin_match.group(1), begin_match.group(2))\n start_map, end_map, word_map = prediction_map[doc_key]\n word_index = 0\n output_file.write(line)\n output_file.write(\"\\n\")\n else:\n assert get_doc_key(row[0], row[1]) == doc_key\n coref_list = []\n if word_index in end_map:\n for cluster_id in end_map[word_index]:\n coref_list.append(\"{})\".format(cluster_id))\n if word_index in word_map:\n for cluster_id in word_map[word_index]:\n coref_list.append(\"({})\".format(cluster_id))\n if word_index in start_map:\n for cluster_id in start_map[word_index]:\n coref_list.append(\"({}\".format(cluster_id))\n\n if len(coref_list) == 0:\n row[-1] = \"-\"\n else:\n row[-1] = \"|\".join(coref_list)\n\n output_file.write(\" \".join(row))\n output_file.write(\"\\n\")\n word_index += 1\n\n\ndef official_conll_eval(gold_path, predicted_path, metric, official_stdout=False):\n cmd = [\"perl\", os.path.join(REPO_PATH, \"conll-2012/scorer/v8.01/scorer.pl\"), metric, gold_path, predicted_path, \"none\"]\n process = subprocess.Popen(cmd, stdout=subprocess.PIPE)\n stdout, stderr = process.communicate()\n process.wait()\n\n stdout = stdout.decode(\"utf-8\")\n if stderr is not None:\n print(stderr)\n\n if official_stdout:\n print(\"Official result for {}\".format(metric))\n print(stdout)\n\n coref_results_match = re.match(COREF_RESULTS_REGEX, stdout)\n recall = float(coref_results_match.group(1))\n precision = float(coref_results_match.group(2))\n f1 = float(coref_results_match.group(3))\n return {\"r\": recall, \"p\": precision, \"f\": f1}\n\n\ndef evaluate_conll(gold_path, predictions, subtoken_maps, official_stdout=False):\n with tempfile.NamedTemporaryFile(delete=False, mode=\"w\") as prediction_file:\n with open(gold_path, \"r\") as gold_file:\n output_conll(gold_file, prediction_file, predictions, subtoken_maps)\n print(\"Predicted conll file: {}\".format(prediction_file.name))\n return {m: official_conll_eval(gold_file.name, prediction_file.name, m, official_stdout) for m in\n (\"muc\", \"bcub\", \"ceafe\")}\n"
},
{
"path": "data_utils/lowercase_vocab.txt",
"content": 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},
{
"path": "data_utils/uppercase_vocab.txt",
"content": "[PAD]\n[unused1]\n[unused2]\n[unused3]\n[unused4]\n[unused5]\n[unused6]\n[unused7]\n[unused8]\n[unused9]\n[unused10]\n[unused11]\n[unused12]\n[unused13]\n[unused14]\n[unused15]\n[unused16]\n[unused17]\n[unused18]\n[unused19]\n[unused20]\n[unused21]\n[unused22]\n[unused23]\n[unused24]\n[unused25]\n[unused26]\n[unused27]\n[unused28]\n[unused29]\n[unused30]\n[unused31]\n[unused32]\n[unused33]\n[unused34]\n[unused35]\n[unused36]\n[unused37]\n[unused38]\n[unused39]\n[unused40]\n[unused41]\n[unused42]\n[unused43]\n[unused44]\n[unused45]\n[unused46]\n[unused47]\n[unused48]\n[unused49]\n[unused50]\n[unused51]\n[unused52]\n[unused53]\n[unused54]\n[unused55]\n[unused56]\n[unused57]\n[unused58]\n[unused59]\n[unused60]\n[unused61]\n[unused62]\n[unused63]\n[unused64]\n[unused65]\n[unused66]\n[unused67]\n[unused68]\n[unused69]\n[unused70]\n[unused71]\n[unused72]\n[unused73]\n[unused74]\n[unused75]\n[unused76]\n[unused77]\n[unused78]\n[unused79]\n[unused80]\n[unused81]\n[unused82]\n[unused83]\n[unused84]\n[unused85]\n[unused86]\n[unused87]\n[unused88]\n[unused89]\n[unused90]\n[unused91]\n[unused92]\n[unused93]\n[unused94]\n[unused95]\n[unused96]\n[unused97]\n[unused98]\n[unused99]\n[UNK]\n[CLS]\n[SEP]\n[MASK]\n[unused100]\n[unused101]\n!\n\"\n#\n$\n%\n&\n'\n(\n)\n*\n+\n,\n-\n.\n/\n0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n:\n;\n<\n=\n>\n?\n@\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n[\n\\\n]\n^\n_\n`\na\nb\nc\nd\ne\nf\ng\nh\ni\nj\nk\nl\nm\nn\no\np\nq\nr\ns\nt\nu\nv\nw\nx\ny\nz\n{\n|\n}\n~\n¡\n¢\n£\n¥\n§\n¨\n©\nª\n«\n¬\n®\n°\n±\n²\n³\n´\nµ\n¶\n·\n¹\nº\n»\n¼\n½\n¾\n¿\nÀ\nÁ\nÂ\nÄ\nÅ\nÆ\nÇ\nÈ\nÉ\nÍ\nÎ\nÑ\nÓ\nÖ\n×\nØ\nÚ\nÜ\nÞ\nß\nà\ná\nâ\nã\nä\nå\næ\nç\nè\né\nê\në\nì\ní\nî\nï\nð\nñ\nò\nó\nô\nõ\nö\n÷\nø\nù\nú\nû\nü\ný\nþ\nÿ\nĀ\nā\nă\ną\nĆ\nć\nČ\nč\nď\nĐ\nđ\nē\nė\nę\ně\nğ\nġ\nĦ\nħ\nĩ\nĪ\nī\nİ\nı\nļ\nĽ\nľ\nŁ\nł\nń\nņ\nň\nŋ\nŌ\nō\nŏ\nő\nŒ\nœ\nř\nŚ\nś\nŞ\nş\nŠ\nš\nŢ\nţ\nť\nũ\nū\nŭ\nů\nű\nų\nŵ\nŷ\nź\nŻ\nż\nŽ\nž\nƏ\nƒ\nơ\nư\nǎ\nǐ\nǒ\nǔ\nǫ\nȘ\nș\nȚ\nț\nɐ\nɑ\nɔ\nɕ\nə\nɛ\nɡ\nɣ\nɨ\nɪ\nɲ\nɾ\nʀ\nʁ\nʂ\nʃ\nʊ\nʋ\nʌ\nʐ\nʑ\nʒ\nʔ\nʰ\nʲ\nʳ\nʷ\nʻ\nʼ\nʾ\nʿ\nˈ\nː\nˡ\nˢ\nˣ\ń\ñ\n̍\n̯\n͡\nΑ\nΒ\nΓ\nΔ\nΕ\nΗ\nΘ\nΙ\nΚ\nΛ\nΜ\nΝ\nΟ\nΠ\nΣ\nΤ\nΦ\nΧ\nΨ\nΩ\nά\nέ\nή\nί\nα\nβ\nγ\nδ\nε\nζ\nη\nθ\nι\nκ\nλ\nμ\nν\nξ\nο\nπ\nρ\nς\nσ\nτ\nυ\nφ\nχ\nψ\nω\nό\nύ\nώ\nІ\nЈ\nА\nБ\nВ\nГ\nД\nЕ\nЖ\nЗ\nИ\nК\nЛ\nМ\nН\nО\nП\nР\nС\nТ\nУ\nФ\nХ\nЦ\nЧ\nШ\nЭ\nЮ\nЯ\nа\nб\nв\nг\nд\nе\nж\nз\nи\nй\nк\nл\nм\nн\nо\nп\nр\nс\nт\nу\nф\nх\nц\nч\nш\nщ\nъ\nы\nь\nэ\nю\nя\nё\nі\nї\nј\nњ\nћ\nԱ\nՀ\nա\nե\nի\nկ\nմ\nյ\nն\nո\nս\nտ\nր\nւ\nְ\nִ\nֵ\nֶ\nַ\nָ\nֹ\nּ\nא\nב\nג\nד\nה\nו\nז\nח\nט\nי\nכ\nל\nם\nמ\nן\nנ\nס\nע\nפ\nצ\nק\nר\nש\nת\n،\nء\nآ\nأ\nإ\nئ\nا\nب\nة\nت\nث\nج\nح\nخ\nد\nذ\nر\nز\nس\nش\nص\nض\nط\nظ\nع\nغ\nف\nق\nك\nل\nم\nن\nه\nو\nى\nي\nَ\nِ\nٹ\nپ\nچ\nک\nگ\nہ\nی\nے\nं\nआ\nक\nग\nच\nज\nण\nत\nद\nध\nन\nप\nब\nभ\nम\nय\nर\nल\nव\nश\nष\nस\nह\nा\nि\nी\nु\nे\nो\n्\n।\n॥\nআ\nই\nএ\nও\nক\nখ\nগ\nচ\nছ\nজ\nট\nত\nথ\nদ\nধ\nন\nপ\nব\nম\nয\nর\nল\nশ\nস\nহ\n়\nা\nি\nী\nু\nে\nো\n্\nয়\nக\nத\nப\nம\nய\nர\nல\nவ\nா\nி\nு\n்\nร\n་\nག\nང\nད\nན\nབ\nམ\nར\nལ\nས\nི\nུ\nེ\nོ\nა\nე\nი\nლ\nნ\nო\nრ\nს\nᴬ\nᴵ\nᵀ\nᵃ\nᵇ\nᵈ\nᵉ\nᵍ\nᵏ\nᵐ\nᵒ\nᵖ\nᵗ\nᵘ\nᵢ\nᵣ\nᵤ\nᵥ\nᶜ\nᶠ\nḍ\nḤ\nḥ\nḨ\nḩ\nḳ\nṃ\nṅ\nṇ\nṛ\nṣ\nṭ\nạ\nả\nấ\nầ\nẩ\nậ\nắ\nế\nề\nể\nễ\nệ\nị\nọ\nố\nồ\nổ\nộ\nớ\nờ\nợ\nụ\nủ\nứ\nừ\nử\nữ\nự\nỳ\nỹ\nἀ\nἐ\nὁ\nὐ\nὰ\nὶ\nὸ\nῆ\nῖ\nῦ\nῶ\n‐\n‑\n‒\n–\n—\n―\n‖\n‘\n’\n‚\n“\n”\n„\n†\n‡\n•\n…\n‰\n′\n″\n⁄\n⁰\nⁱ\n⁴\n⁵\n⁶\n⁷\n⁸\n⁹\n⁺\n⁻\nⁿ\n₀\n₁\n₂\n₃\n₄\n₅\n₆\n₇\n₈\n₉\n₊\n₍\n₎\nₐ\nₑ\nₒ\nₓ\nₕ\nₖ\nₘ\nₙ\nₚ\nₛ\nₜ\n₤\n€\n₱\n₹\nℓ\n№\nℝ\n⅓\n←\n↑\n→\n↔\n⇌\n⇒\n∂\n∈\n−\n∗\n∘\n√\n∞\n∧\n∨\n∩\n∪\n≈\n≠\n≡\n≤\n≥\n⊂\n⊆\n⊕\n⋅\n─\n│\n■\n●\n★\n☆\n☉\n♠\n♣\n♥\n♦\n♭\n♯\n⟨\n⟩\nⱼ\n、\n。\n《\n》\n「\n」\n『\n』\n〜\nい\nう\nえ\nお\nか\nき\nく\nけ\nこ\nさ\nし\nす\nせ\nそ\nた\nち\nつ\nて\nと\nな\nに\nの\nは\nひ\nま\nみ\nむ\nめ\nも\nや\nゆ\nよ\nら\nり\nる\nれ\nん\nア\nィ\nイ\nウ\nエ\nオ\nカ\nガ\nキ\nク\nグ\nコ\nサ\nシ\nジ\nス\nズ\nタ\nダ\nッ\nテ\nデ\nト\nド\nナ\nニ\nハ\nバ\nパ\nフ\nブ\nプ\nマ\nミ\nム\nャ\nュ\nラ\nリ\nル\nレ\nロ\nン\n・\nー\n一\n三\n上\n下\n中\n事\n二\n井\n京\n人\n亻\n仁\n佐\n侍\n光\n公\n力\n北\n十\n南\n原\n口\n史\n司\n吉\n同\n和\n囗\n国\n國\n土\n城\n士\n大\n天\n太\n夫\n女\n子\n宀\n安\n宮\n宿\n小\n尚\n山\n島\n川\n州\n平\n年\n心\n愛\n戸\n文\n新\n方\n日\n明\n星\n書\n月\n木\n本\n李\n村\n東\n松\n林\n正\n武\n氏\n水\n氵\n江\n河\n海\n版\n犬\n王\n生\n田\n白\n皇\n省\n真\n石\n社\n神\n竹\n美\n義\n花\n藤\n西\n谷\n車\n辶\n道\n郎\n郡\n部\n野\n金\n長\n門\n陽\n青\n食\n馬\n高\n龍\n龸\n사\n씨\n의\n이\n한\nfi\nfl\n！\n（\n）\n，\n－\n／\n：\nthe\nof\nand\nto\nin\nwas\nThe\nis\nfor\nas\non\nwith\nthat\n##s\nhis\nby\nhe\nat\nfrom\nit\nher\nHe\nhad\nan\nwere\nyou\nbe\nIn\nshe\nare\nbut\nwhich\nIt\nnot\nor\nhave\nmy\nhim\none\nthis\nme\nhas\nalso\nup\ntheir\nfirst\nout\nwho\nbeen\nthey\nShe\ninto\nall\nwould\nits\n##ing\ntime\ntwo\n##a\n##e\nsaid\nabout\nwhen\nover\nmore\nother\ncan\nafter\nback\nthem\nthen\n##ed\nthere\nlike\nso\nonly\n##n\ncould\n##d\n##i\n##y\nwhat\nno\n##o\nwhere\nThis\nmade\nthan\nif\nYou\n##ly\nthrough\nwe\nbefore\n##r\njust\nsome\n##er\nyears\ndo\nNew\n##t\ndown\nbetween\nnew\nnow\nwill\nthree\nmost\nOn\naround\nyear\nused\nsuch\nbeing\nwell\nduring\nThey\nknow\nagainst\nunder\nlater\ndid\npart\nknown\noff\nwhile\nHis\nre\n...\n##l\npeople\nuntil\nway\nAmerican\ndidn\nUni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partment\ntable\nMusic\ncurrent\n31\nfeatures\nspecial\nevents\ncharacter\nTwo\nsquare\nsold\ndebut\n##v\nprocess\nAlthough\nSince\n##ka\n40\nCentral\ncurrently\neducation\nplaced\nlot\nChina\nquickly\nforward\nseven\n##ling\nEurope\narm\nperformed\nJapanese\n1991\nHenry\nNow\nDr\n##ion\nweek\nGroup\nmyself\nbig\nUK\nWashington\nten\ndeep\n1990\nClub\nJapan\nspace\nLa\ndirected\nsmile\nepisode\nhours\nwhole\n##de\n##less\nWhy\nwouldn\ndesigned\nstrong\ntraining\nchanged\nSociety\nstage\ninvolved\nhadn\ntowards\nleading\npolice\neight\nkept\nInstitute\nstudy\nlargest\nchild\neventually\nprivate\nmodern\nCourt\nthroughout\ngetting\noriginally\nattack\n##E\ntalk\nGreat\nlonger\nsongs\nalone\n##ine\nwide\ndead\nwalked\nshot\n##ri\nOh\nforce\n##st\nArt\ntoday\nfriends\nIsland\nRichard\n1989\ncenter\nconstruction\nbelieve\nsize\nWhite\nship\ncompleted\n##B\ngone\nJust\nrock\nsat\n##R\nradio\nbelow\nentire\nfamilies\nleague\nincludes\ntype\nlived\nofficial\nrange\nhold\nfeatured\nMost\n##ter\npresident\npassed\nmeans\n##f\nforces\nlips\nMary\nDo\nguitar\n##ce\nfood\nwall\nOf\nspent\nIts\nperformance\nhear\n##P\nWestern\nreported\nsister\n##et\nmorning\n##M\nespecially\n##ive\nMinister\nitself\npost\nbit\ngroups\n1988\n##tion\nBlack\n##ng\nWell\nraised\nsometimes\nCanadian\nParis\nSpanish\nreplaced\nschools\nAcademy\nleaving\ncentral\nfemale\nChristian\nJack\nwhose\ncollege\nonto\nprovided\n##D\n##ville\nplayers\nactually\nstopped\n##son\nMuseum\ndoesn\n##ts\nbooks\nfight\nallowed\n##ur\nbeginning\nRecords\nawarded\nparents\ncoach\n##os\nRed\nsaying\n##ck\nSmith\nYes\nLake\n##L\naircraft\n1987\n##ble\nprevious\nft\naction\nItalian\nAfrican\nhappened\nvocals\nAct\nfuture\ncourt\n##ge\n1986\ndegree\nphone\n##ro\nIs\ncountries\nwinning\nbreath\nLove\nriver\nmatter\nLord\nOther\nlist\nself\nparts\n##ate\nprovide\ncut\nshows\nplan\n1st\ninterest\n##ized\nAfrica\nstated\nSir\nfell\nowned\nearlier\nended\ncompetition\nattention\n1985\nlower\nnearly\nbad\nolder\nstay\nSaint\n##se\ncertain\n1984\nfingers\nblue\ntry\nfourth\nGrand\n##as\nking\n##nt\nmakes\nchest\nmovement\nstates\nmoving\ndata\nintroduced\nmodel\ndate\nsection\nLos\ndeal\n##I\nskin\nentered\nmiddle\nsuccess\nTexas\n##w\nsummer\nisland\n##N\nRepublic\nlength\nhusband\n1980\n##ey\nreason\nanyone\nforced\nvia\nbase\n500\njob\ncovered\nFestival\nRoman\nsuccessful\nrights\ncover\nMan\nwriting\nIreland\n##F\nrelated\ngoal\ntakes\nbuildings\ntrue\nweeks\n1983\nBecause\nopening\nnovel\nISBN\nmeet\ngold\n##ous\nmid\nkm²\nstanding\nFootball\nChicago\nshook\nwhom\n##ki\n1982\nDay\nfeeling\nscored\nboy\nhigher\nForce\nleader\nheavy\nfall\nquestion\nsense\narmy\nSecond\nenergy\nmeeting\nthemselves\nkill\n##am\nboard\ncensus\n##ya\n##ns\nmine\nmeant\nmarket\nrequired\nbattle\ncampaign\nattended\napproximately\nKingdom\nruns\nactive\n##ha\ncontract\nclear\npreviously\nhealth\n1979\nArts\ncomplete\nCatholic\ncouple\nunits\n##ll\n##ty\nCommittee\nshoulder\nsea\nsystems\nlisted\n##O\ncaught\ntournament\n##G\nnorthern\nauthor\nFilm\nYour\n##men\nholding\noffered\npersonal\n1981\nsouthern\nartist\ntraditional\nstudio\n200\ncapital\n##ful\nregular\nask\ngiving\norganization\nmonth\nnews\nAre\nread\nmanaged\nhelped\nstudied\nstudent\ndefeated\nnatural\nindustry\nYear\nnoted\ndecision\nGovernment\nquite\n##id\nsmiled\n1972\nMaybe\ntracks\n##ke\nMark\nal\nmedia\nengine\nhour\nTheir\nrelationship\nplays\nproperty\nstructure\n1976\nago\nHill\nMartin\n1978\nready\nMany\nLike\nBay\nimmediately\ngenerally\nItaly\nGreek\npractice\ncaused\ndivision\nsignificant\nJoseph\nspeed\nLet\nthinking\ncompletely\n1974\nprimary\nmostly\n##field\n##K\n1975\n##to\nEven\nwriter\n##led\ndropped\nmagazine\ncollection\nunderstand\nroute\nhighest\nparticular\nfilms\nlines\nnetwork\nScience\nloss\ncarried\ndirection\ngreen\n1977\nlocation\nproducer\naccording\nWomen\nQueen\nneck\nthus\nindependent\nview\n1970\nAngeles\nSoviet\ndistance\nproblem\nBoard\ntour\nwestern\nincome\nappearance\naccess\nMexico\nnodded\nstreet\nsurface\narrived\nbelieved\nOld\n1968\n1973\nbecoming\nwhether\n1945\nfigure\nsinger\nstand\nFollowing\nissue\nwindow\nwrong\npain\neveryone\nlives\nissues\npark\nslowly\nla\nact\n##va\nbring\nLee\noperations\nkey\ncomes\nfine\ncold\nfamous\nNavy\n1971\nMe\nadditional\nindividual\n##ner\nZealand\ngoals\ncounty\ncontains\nService\nminute\n2nd\nreach\ntalking\nparticularly\n##ham\nmovie\nDirector\nglass\npaper\nstudies\n##co\nrailway\nstandard\nEducation\n45\nrepresented\nChief\nLouis\nlaunched\nStar\nterms\n60\n1969\nexperience\nwatched\nAnother\nPress\nTom\nstaff\nstarting\nsubject\nbreak\nVirginia\nnine\neye\n##age\nevidence\nfoot\n##est\ncompanies\nPrince\n##V\ngun\ncreate\nBig\nPeople\nguy\nGreen\nsimply\nnumerous\n##line\nincreased\ntwenty\n##ga\n##do\n1967\naward\nofficer\nstone\nBefore\nmaterial\nNorthern\ngrew\nmale\nplant\nLife\nlegs\nstep\nAl\nunit\n35\nexcept\nanswer\n##U\nreport\nresponse\nEdward\ncommercial\nedition\ntrade\nscience\n##ca\nIrish\nLaw\nshown\nrate\nfailed\n##ni\nrema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ball\nhorse\nways\nquestions\nprotect\ngas\nactivity\nyounger\nbottom\nfounder\nScottish\nscreen\ntreatment\neasily\ncom\n##house\ndedicated\nMaster\nwarm\nNight\nGeorgia\nLong\nvon\n##me\nperfect\nwebsite\n1960s\npiano\nefforts\n##ide\nTony\nsort\noffers\nDevelopment\nSimon\nexecutive\n##nd\nsave\nOver\nSenate\n1951\n1990s\ndraw\nmaster\nPolice\n##ius\nrenamed\nboys\ninitial\nprominent\ndamage\nCo\n##ov\n##za\nonline\nbegin\noccurred\ncaptured\nyouth\nTop\naccount\ntells\nJustice\nconducted\nforest\n##town\nbought\nteeth\nJersey\n##di\npurchased\nagreement\nMichigan\n##ure\ncampus\nprison\nbecomes\nproduct\nsecret\nguess\nRoute\nhuge\ntypes\ndrums\n64\nsplit\ndefeat\nestate\nhousing\n##ot\nbrothers\nCoast\ndeclared\nhappen\ntitled\ntherefore\nsun\ncommonly\nalongside\nStadium\nlibrary\nHome\narticle\nsteps\ntelling\nslow\nassigned\nrefused\nlaughed\nwants\nNick\nwearing\nRome\nOpen\n##ah\nHospital\npointed\nTaylor\nlifted\nescape\nparticipated\n##j\ndrama\nparish\nSanta\n##per\norganized\nmass\npick\nAirport\ngets\nLibrary\nunable\npull\nLive\n##ging\nsurrounding\n##ries\nfocused\nAdam\nfacilities\n##ning\n##ny\n38\n##ring\nnotable\nera\nconnected\ngained\noperating\nlaid\nRegiment\nbranch\ndefined\nChristmas\nmachine\nFour\nacademic\nIran\nadopted\nconcept\nMen\ncompared\nsearch\ntraffic\nMax\nMaria\ngreater\n##ding\nwidely\n##burg\nserves\n1938\n37\nGo\nhotel\nshared\ntypically\nscale\n1936\nleg\nsuffered\nyards\npieces\nMinistry\nWilson\nepisodes\nempty\n1918\nsafety\ncontinues\nyellow\nhistoric\nsettlement\n400\nCome\nCorporation\nenemy\ncontent\npicture\nevening\nterritory\nmethod\ntrial\nsolo\ndriver\nHere\n##ls\nentrance\nPrize\nspring\nwhatever\n##ent\n75\n##ji\nreading\nArthur\n##cy\nOur\nclothes\nPrime\nIllinois\nKong\ncode\n##ria\nsit\nHarry\nFederal\nchosen\nadministration\nbodies\nbegins\nstomach\nThough\nseats\nHong\ndensity\nSun\nleaders\nField\nmuseum\nchart\nplatform\nlanguages\n##ron\nbirth\nholds\nGold\n##un\nfish\ncombined\n##ps\n4th\n1937\nlargely\ncaptain\ntrust\nGame\nvan\nboat\nOxford\nbasic\nbeneath\nIslands\npainting\nnice\nToronto\npath\nmales\nsources\nblock\nconference\nparties\nmurder\nclubs\ncrowd\ncalling\nAbout\nBusiness\npeace\nknows\nlake\nspeaking\nstayed\nBrazil\nallowing\nBorn\nunique\nthick\nTechnology\n##que\nreceive\ndes\nsemi\nalive\nnoticed\nformat\n##ped\ncoffee\ndigital\n##ned\nhanded\nguard\ntall\nfaced\nsetting\nplants\npartner\nclaim\nreduced\ntemple\nanimals\ndetermined\nclasses\n##out\nestimated\n##ad\nOlympic\nproviding\nMassachusetts\nlearned\nInc\nPhiladelphia\nSocial\ncarry\n42\npossibly\nhosted\ntonight\nrespectively\nToday\nshape\nMount\nroles\ndesignated\nbrain\netc\nKorea\nthoughts\nBrian\nHighway\ndoors\nbackground\ndrew\nmodels\nfootballer\ntone\nturns\n1935\nquiet\ntower\nwood\nbus\nwrite\nsoftware\nweapons\nflat\nmarked\n1920\nnewly\ntight\nEric\nfinger\nJournal\nFC\nVan\nrise\ncritical\nAtlantic\ngranted\nreturning\ncommunities\nhumans\nquick\n39\n48\nranked\nsight\npop\nSwedish\nStephen\ncard\nanalysis\nattacked\n##wa\nSunday\nidentified\nJason\nchampion\nsituated\n1930\nexpanded\ntears\n##nce\nreaching\nDavis\nprotection\nEmperor\npositions\nnominated\nBridge\ntax\ndress\nallows\navoid\nleadership\nkilling\nactress\nguest\nsteel\nknowing\nelectric\ncells\ndisease\ngrade\nunknown\n##ium\nresulted\nPakistan\nconfirmed\n##ged\ntongue\ncovers\n##Y\nroof\nentirely\napplied\nvotes\ndrink\ninterview\nexchange\nTownship\nreasons\n##ised\npage\ncalls\ndog\nagent\nnose\nteaching\n##ds\n##ists\nadvanced\nwish\nGolden\nexisting\nvehicle\ndel\n1919\ndevelop\nattacks\npressed\nSports\nplanning\nresulting\nfacility\nSarah\nnotes\n1933\nClass\nHistoric\nwinter\n##mo\naudience\nCommunity\nhousehold\nNetherlands\ncreation\n##ize\nkeeping\n1914\nclaims\ndry\nguys\nopposite\n##ak\nexplained\nOntario\nsecondary\ndifference\nFrancis\nactions\norganizations\nyard\nanimal\nUp\nLewis\ntitles\nSeveral\n1934\nRyan\n55\nSupreme\nrolled\n1917\ndistribution\nfigures\nafraid\nrural\nyourself\n##rt\nsets\nbarely\nInstead\npassing\nawards\n41\nsilence\nauthority\noccupied\nenvironment\nwindows\nengineering\nsurprised\nflying\ncrime\nreports\nMountain\npowers\ndriving\nsucceeded\nreviews\n1929\nHead\nmissing\nSong\nJesus\nopportunity\ninspired\nends\nalbums\nconversation\nimpact\ninjury\nsurprise\nbillion\nlearning\nheavily\noldest\nunion\ncreating\n##ky\nfestival\nliterature\nletters\nsexual\n##tte\napartment\nFinal\ncomedy\nnation\norders\n##sen\ncontemporary\nPower\ndrawn\nexistence\nconnection\n##ating\nPost\nJunior\nremembered\nmessage\nMedal\ncastle\nnote\nengineer\nsounds\nBeach\ncrossed\n##dy\near\nscientific\nsales\n##ai\ntheme\nstarts\nclearly\n##ut\ntrouble\n##gan\nbag\n##han\nBC\nsons\n1928\nsilent\nversions\ndaily\nStudies\nending\nRose\nguns\n1932\nheadquarters\nreference\nobtained\nSquadron\nconcert\nnone\ndu\nAmong\n##don\nprevent\nMember\nanswered\nstaring\nBetween\n##lla\nportion\ndrug\nliked\nassociation\nperformances\nNations\nformation\nCastle\nlose\nlear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ntravels\nsamples\naccurate\nphysically\nSony\nNashville\n##sville\n##lic\n##og\nProducer\nLucky\ntough\nStanford\nresort\nrepeatedly\neyebrows\nFar\nchoir\ncommenced\n##ep\n##ridge\nrage\nswing\nsequel\nheir\nbuses\nad\nGrove\n##late\n##rick\nupdated\n##SA\nDelaware\n##fa\nAthletics\nwarmth\nOff\nexcitement\nverse\nProtection\nVilla\ncorruption\nintellectual\nJenny\n##lyn\nmystery\nprayer\nhealthy\n##ologist\nBear\nlab\nErnest\nRemix\nregister\nbasement\nMontgomery\nconsistent\ntier\n1855\nPreston\nBrooks\n##maker\nvocalist\nlaboratory\ndelayed\nwheels\nrope\nbachelor\npitcher\nBlock\nNevertheless\nsuspect\nefficiency\nNebraska\nsiege\nFBI\nplanted\n##AC\nNewton\nbreeding\n##ain\neighteen\nArgentine\nencounter\nservant\n1858\nelder\nShadow\nEpisode\nfabric\ndoctors\nsurvival\nremoval\nchemistry\nvolunteers\nKane\nvariant\narrives\nEagle\nLeft\n##fe\nJo\ndivorce\n##ret\nyesterday\nBryan\nhandling\ndiseases\ncustomer\nSheriff\nTiger\nHarper\n##oi\nresting\nLinda\nSheffield\ngasped\nsexy\neconomics\nalien\ntale\nfootage\nLiberty\nyeah\nfundamental\nGround\nflames\nActress\nphotographer\nMaggie\nAdditional\njoke\ncustom\nSurvey\nAbu\nsilk\nconsumption\nEllis\nbread\n##uous\nengagement\nputs\nDog\n##hr\npoured\nguilt\nCDP\nboxes\nhardware\nclenched\n##cio\nstem\narena\nextending\n##com\nexamination\nSteel\nencountered\nrevised\n140\npicking\nCar\nhasn\nMinor\npride\nRoosevelt\nboards\n##mia\nblocked\ncurious\ndrag\nnarrative\nbrigade\nPrefecture\nmysterious\nnamely\nconnects\nDevil\nhistorians\nCHAPTER\nquit\ninstallation\nGolf\nempire\nelevated\n##eo\nreleasing\nBond\n##uri\nharsh\nban\n##BA\ncontracts\ncloth\npresents\nstake\nchorus\n##eau\nswear\n##mp\nallies\ngenerations\nMotor\nmeter\npen\nwarrior\nveteran\n##EC\ncomprehensive\nmissile\ninteraction\ninstruction\nRenaissance\nrested\nDale\nfix\nfluid\nles\ninvestigate\nloaded\nwidow\nexhibit\nartificial\nselect\nrushing\ntasks\nsignature\nnowhere\nEngineer\nfeared\nPrague\nbother\nextinct\ngates\nBird\nclimbing\nheels\nstriking\nartwork\nhunt\nawake\n##hin\nFormula\nthereby\ncommitment\nimprisoned\nBeyond\n##MA\ntransformed\nAgriculture\nLow\nMovie\nradical\ncomplicated\nYellow\nAuckland\nmansion\ntenth\nTrevor\npredecessor\n##eer\ndisbanded\nsucked\ncircular\nwitch\ngaining\nlean\nBehind\nillustrated\nrang\ncelebrate\nbike\nconsist\nframework\n##cent\nShane\nowns\n350\ncomprises\ncollaborated\ncolleagues\n##cast\nengage\nfewer\n##ave\n1856\nobservation\ndiplomatic\nlegislature\nimprovements\nInterstate\ncraft\nMTV\nmartial\nadministered\njet\napproaching\npermanently\nattraction\nmanuscript\nnumbered\nHappy\nAndrea\nshallow\nGothic\nAnti\n##bad\nimprovement\ntrace\npreserve\nregardless\nrode\ndies\nachievement\nmaintaining\nHamburg\nspine\n##air\nflowing\nencourage\nwidened\nposts\n##bound\n125\nSoutheast\nSantiago\n##bles\nimpression\nreceiver\nSingle\nclosure\n##unt\ncommunist\nhonors\nNorthwest\n105\n##ulated\ncared\nun\nhug\nmagnetic\nseeds\ntopic\nperceived\nprey\nprevented\nMarvel\nEight\nMichel\nTransportation\nrings\nGate\n##gne\nByzantine\naccommodate\nfloating\n##dor\nequation\nministry\n##ito\n##gled\nRules\nearthquake\nrevealing\nBrother\nCeltic\nblew\nchairs\nPanama\nLeon\nattractive\ndescendants\nCare\nAmbassador\ntours\nbreathed\nthreatening\n##cho\nsmiles\nLt\nBeginning\n##iness\nfake\nassists\nfame\nstrings\nMobile\nLiu\nparks\nhttp\n1852\nbrush\nAunt\nbullet\nconsciousness\n##sta\n##ther\nconsequences\ngather\ndug\n1851\nbridges\nDoug\n##sion\nArtists\nignore\nCarol\nbrilliant\nradiation\ntemples\nbasin\nclouds\n##cted\nStevens\nspite\nsoap\nconsumer\nDamn\nSnow\nrecruited\n##craft\nAdvanced\ntournaments\nQuinn\nundergraduate\nquestioned\nPalmer\nAnnual\nOthers\nfeeding\nSpider\nprinting\n##orn\ncameras\nfunctional\nChester\nreaders\nAlpha\nuniversal\nFaith\nBrandon\nFrançois\nauthored\nRing\nel\naims\nathletic\npossessed\nVermont\nprogrammes\n##uck\nbore\nFisher\nstatements\nshed\nsaxophone\nneighboring\npronounced\nbarrel\nbags\n##dge\norganisations\npilots\ncasualties\nKenneth\n##brook\nsilently\nMalcolm\nspan\nEssex\nanchor\n##hl\nvirtual\nlessons\nHenri\nTrump\nPage\npile\nlocomotive\nwounds\nuncomfortable\nsustained\nDiana\nEagles\n##pi\n2000s\ndocumented\n##bel\nCassie\ndelay\nkisses\n##ines\nvariation\n##ag\ngrowled\n##mark\n##ways\nLeslie\nstudios\nFriedrich\naunt\nactively\narmor\neaten\nhistorically\nBetter\npurse\nhoney\nratings\n##ée\nnaturally\n1840\npeer\nKenny\nCardinal\ndatabase\nLooking\nrunners\nhandsome\nDouble\nPA\n##boat\n##sted\nprotecting\n##jan\nDiamond\nconcepts\ninterface\n##aki\nWatch\nArticle\nColumbus\ndialogue\npause\n##rio\nextends\nblanket\npulse\n1853\naffiliate\nladies\nRonald\ncounted\nkills\ndemons\n##zation\nAirlines\nMarco\nCat\ncompanion\nmere\nYugoslavia\nForum\nAllan\npioneer\nCompetition\nMethodist\npatent\nnobody\nStockholm\n##ien\nregulation\n##ois\naccomplished\n##itive\nwashed\nsake\nVladimir\ncrops\nprestigious\nhumor\nSally\nlabour\ntributary\ntrap\naltered\nexamined\nMumbai\nbombing\nAsh\n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\n##rlin\nAntioch\nGao\nDíaz\n1760\nSparks\n##tien\n##bidae\nrehearsal\n##bbs\nbotanical\n##hers\ncompensate\nwholesale\nSeville\nshareholder\nprediction\nastronomical\nReddy\nhardest\ncircling\nwhereabouts\ntermination\nRep\nAssistance\nDramatic\nHerb\n##ghter\nclimbs\n188\nPoole\n301\n##pable\nwit\n##istice\nWalters\nrelying\nJakob\n##redo\nproceeding\nLangley\naffiliates\nou\n##allo\n##holm\nSamsung\n##ishi\nMissing\nXi\nvertices\nClaus\nfoam\nrestless\n##uating\n##sso\n##ttering\nPhilips\ndelta\nbombed\nCatalogue\ncoaster\nLing\nWillard\nsatire\n410\nComposition\nNet\nOrioles\n##ldon\nfins\nPalatinate\nWoodward\ntease\ntilt\nbrightness\n##70\n##bbling\n##loss\n##dhi\n##uilt\nWhoever\n##yers\nhitter\nElton\nExtension\nace\nAffair\nrestructuring\n##loping\nPaterson\nhi\n##rya\nspouse\nShay\nHimself\npiles\npreaching\n##gical\nbikes\nBrave\nexpulsion\nMirza\nstride\nTrees\ncommemorated\nfamine\nmasonry\nSelena\nWatt\nBanking\nRancho\nStockton\ndip\ntattoos\nVlad\nacquainted\nFlyers\nruthless\nfourteenth\nillustrate\n##akes\nEPA\n##rows\n##uiz\nbumped\nDesigned\nLeaders\nmastered\nManfred\nswirled\nMcCain\n##rout\nArtemis\nrabbi\nflinched\nupgrades\npenetrate\nshipyard\ntransforming\ncaretaker\n##eiro\nMaureen\ntightening\n##founded\nRAM\n##icular\n##mper\n##rung\nFifteen\nexploited\nconsistency\ninterstate\n##ynn\nBridget\ncontamination\nMistress\n##rup\ncoating\n##FP\n##jective\nLibyan\n211\nGemma\ndependence\nshrubs\n##ggled\nGermain\nretaliation\ntraction\n##PP\nDangerous\nterminology\npsychiatrist\n##garten\nhurdles\nNatal\nwasting\nWeir\nrevolves\nstripe\n##reased\npreferences\n##entation\n##lde\n##áil\n##otherapy\nFlame\n##ologies\nviruses\nLabel\nPandora\nveil\n##ogical\nColiseum\nCottage\ncreeping\nJong\nlectured\n##çaise\nshoreline\n##fference\n##hra\nShade\nClock\nFaye\nbilingual\nHumboldt\nOperating\n##fter\n##was\nalgae\ntowed\namphibious\nParma\nimpacted\nsmacked\nPiedmont\nMonsters\n##omb\nMoor\n##lberg\nsinister\nPostal\n178\nDrummond\nSign\ntextbooks\nhazardous\nBrass\nRosemary\nPick\nSit\nArchitect\ntransverse\nCentennial\nconfess\npolling\n##aia\nJulien\n##mand\nconsolidation\nEthel\n##ulse\nseverity\nYorker\nchoreographer\n1840s\n##ltry\nsofter\nversa\n##geny\n##quila\n##jō\nCaledonia\nFriendship\nVisa\nrogue\n##zzle\nbait\nfeather\nincidence\nFoods\nShips\n##uto\n##stead\narousal\n##rote\nHazel\n##bolic\nSwing\n##ej\n##cule\n##jana\n##metry\n##uity\nValuable\n##ₙ\nShropshire\n##nect\n365\nOnes\nrealise\nCafé\nAlbuquerque\n##grown\n##stadt\n209\n##ᵢ\nprefers\nwithstand\nLillian\nMacArthur\nHara\n##fulness\ndomination\n##VO\n##school\nFreddy\nethnicity\n##while\nadorned\nhormone\nCalder\nDomestic\nFreud\nShields\n##phus\n##rgan\nBP\nSegunda\nMustang\n##GI\nBonn\npatiently\nremarried\n##umbria\nCrete\nElephant\nNuremberg\ntolerate\nTyson\n##evich\nProgramming\n##lander\nBethlehem\nsegregation\nConstituency\nquarterly\nblushed\nphotographers\nSheldon\nporcelain\nBlanche\ngoddamn\nlively\n##fused\nbumps\n##eli\ncurated\ncoherent\nprovoked\n##vet\nMadeleine\n##isco\nrainy\nBethel\naccusation\nponytail\ngag\n##lington\nquicker\nscroll\n##vate\nBow\nGender\nIra\ncrashes\nACT\nMaintenance\n##aton\n##ieu\nbitterly\nstrains\nrattled\nvectors\n##arina\n##ishly\n173\nparole\n##nx\namusing\nGonzalez\n##erative\nCaucus\nsensual\nPenelope\ncoefficient\nMateo\n##mani\nproposition\nDuty\nlacrosse\nproportions\nPlato\nprofiles\nBotswana\nBrandt\nreins\nmandolin\nencompassing\n##gens\nKahn\nprop\nsummon\n##MR\n##yrian\n##zaki\nFalling\nconditional\nthy\n##bao\n##ych\nradioactive\n##nics\nNewspaper\n##people\n##nded\nGaming\nsunny\n##look\nSherwood\ncrafted\nNJ\nawoke\n187\ntimeline\ngiants\npossessing\n##ycle\nCheryl\nng\nRuiz\npolymer\npotassium\nRamsay\nrelocation\n##leen\nSociology\n##bana\nFranciscan\npropulsion\ndenote\n##erjee\nregisters\nheadline\nTests\nemerges\nArticles\nMint\nlivery\nbreakup\nkits\nRap\nBrowning\nBunny\n##mington\n##watch\nAnastasia\nZachary\narranging\nbiographical\nErica\nNippon\n##membrance\nCarmel\n##sport\n##xes\nPaddy\n##holes\nIssues\nSpears\ncompliment\n##stro\n##graphs\nCastillo\n##MU\n##space\nCorporal\n##nent\n174\nGentlemen\n##ilize\n##vage\nconvinces\nCarmine\nCrash\n##hashi\nFiles\nDoctors\nbrownish\nsweating\ngoats\n##conductor\nrendition\n##bt\nNL\n##spiration\ngenerates\n##cans\nobsession\n##noy\nDanger\nDiaz\nheats\nRealm\npriorities\n##phon\n1300\ninitiation\npagan\nbursts\narchipelago\nchloride\nScreenplay\nHewitt\nKhmer\nbang\njudgement\nnegotiating\n##ait\nMabel\ndensely\nBoulder\nknob\n430\nAlfredo\n##kt\npitches\n##ées\n##ان\nMacdonald\n##llum\nimply\n##mot\nSmile\nspherical\n##tura\nDerrick\nKelley\nNico\ncortex\nlaunches\ndiffered\nparallels\nNavigation\n##child\n##rming\ncanoe\nforestry\nreinforce\n##mote\nconfirming\ntasting\nscaled\n##resh\n##eting\nUnderstanding\nprevailing\nPearce\nCW\nearnest\nGaius\nasserts\ndenoted\nlandmarks\nChargers\nwarns\n##flies\nJudges\njagged\n##dain\ntails\nHistorian\nMillie\n##sler\n221\n##uard\nabsurd\nDion\n##ially\nmakeshift\nSpecifically\nignorance\nEat\n##ieri\ncomparisons\nforensic\n186\nGiro\nskeptical\ndisciplinary\nbattleship\n##45\nLibby\n520\nOdyssey\nledge\n##post\nEternal\nMissionary\ndeficiency\nsettler\nwonders\n##gai\nraging\n##cis\nRomney\nUlrich\nannexation\nboxers\nsect\n204\nARIA\ndei\nHitchcock\nte\nVarsity\n##fic\nCC\nlending\n##nial\n##tag\n##rdy\n##obe\nDefensive\n##dson\n##pore\nstellar\nLam\nTrials\ncontention\nSung\n##uminous\nPoe\nsuperiority\n##plicate\n325\nbitten\nconspicuous\n##olly\nLila\nPub\nPetit\ndistorted\nISIL\ndistinctly\n##family\nCowboy\nmutant\n##cats\n##week\nChanges\nSinatra\nepithet\nneglect\nInnocent\ngamma\nthrill\nreggae\n##adia\n##ational\n##due\nlandlord\n##leaf\nvisibly\n##ì\nDarlington\nGomez\n##iting\nscarf\n##lade\nHinduism\nFever\nscouts\n##roi\nconvened\n##oki\n184\nLao\nboycott\nunemployed\n##lore\n##ß\n##hammer\nCurran\ndisciples\nodor\n##ygiene\nLighthouse\nPlayed\nwhales\ndiscretion\nYves\n##ceived\npauses\ncoincide\n##nji\ndizzy\n##scopic\nrouted\nGuardians\nKellan\ncarnival\nnasal\n224\n##awed\nMitsubishi\n640\nCast\nsilky\nProjects\njoked\nHuddersfield\nRothschild\nzu\n##olar\nDivisions\nmildly\n##eni\n##lge\nAppalachian\nSahara\npinch\n##roon\nwardrobe\n##dham\n##etal\nBubba\n##lini\n##rumbling\nCommunities\nPoznań\nunification\nBeau\nKris\nSV\nRowing\nMinh\nreconciliation\n##saki\n##sor\ntaped\n##reck\ncertificates\ngubernatorial\nrainbow\n##uing\nlitter\n##lique\n##oted\nButterfly\nbenefited\nImages\ninduce\nBalkans\nVelvet\n##90\n##xon\nBowman\n##breaker\npenis\n##nitz\n##oint\n##otive\ncrust\n##pps\norganizers\nOutdoor\nnominees\n##rika\nTX\n##ucks\nProtestants\n##imation\nappetite\nBaja\nawaited\n##points\nwindshield\n##igh\n##zled\nBrody\nBuster\nstylized\nBryce\n##sz\nDollar\nvest\nmold\nounce\nok\nreceivers\n##uza\nPurdue\nHarrington\nHodges\ncaptures\n##ggio\nReservation\n##ssin\n##tman\ncosmic\nstraightforward\nflipping\nremixed\n##athed\nGómez\nLim\nmotorcycles\neconomies\nowning\nDani\n##rosis\nmyths\nsire\nkindly\n1768\nBean\ngraphs\n##mee\n##RO\n##geon\npuppy\nStephenson\nnotified\n##jer\nWatching\n##rama\nSino\nurgency\nIslanders\n##mash\nPlata\nfumble\n##chev\n##stance\n##rack\n##she\nfacilitated\nswings\nakin\nenduring\npayload\n##phine\nDeputies\nmurals\n##tooth\n610\nJays\neyeing\n##quito\ntransparency\n##cote\nTimor\nnegatively\n##isan\nbattled\n##fected\nthankful\nRage\nhospitality\nincorrectly\n207\nentrepreneurs\n##cula\n##wley\nhedge\n##cratic\nCorpus\nOdessa\nWhereas\n##ln\nfetch\nhappier\nAmherst\nbullying\ngraceful\nHeight\nBartholomew\nwillingness\nqualifier\n191\nSyed\nWesleyan\nLayla\n##rrence\nWebber\n##hum\nRat\n##cket\n##herence\nMonterey\ncontaminated\nBeside\nMustafa\nNana\n213\n##pruce\nReason\n##spense\nspike\n##gé\nAU\ndisciple\ncharcoal\n##lean\nformulated\nDiesel\nMariners\naccreditation\nglossy\n1800s\n##ih\nMainz\nunison\nMarianne\nshear\noverseeing\nvernacular\nbowled\n##lett\nunpopular\n##ckoned\n##monia\nGaston\n##TI\n##oters\nCups\n##bones\n##ports\nMuseo\nminors\n1773\nDickens\n##EL\n##NBC\nPresents\nambitions\naxes\nRío\nYukon\nbedside\nRibbon\nUnits\nfaults\nconceal\n##lani\nprevailed\n214\nGoodwin\nJaguar\ncrumpled\nCullen\nWireless\nceded\nremotely\nBin\nmocking\nstraps\nceramics\n##avi\n##uding\n##ader\nTaft\ntwenties\n##aked\nProblem\nquasi\nLamar\n##ntes\n##avan\nBarr\n##eral\nhooks\nsa\n##ône\n194\n##ross\nNero\nCaine\ntrance\nHomeland\nbenches\nGuthrie\ndismiss\n##lex\nCésar\nfoliage\n##oot\n##alty\nAssyrian\nAhead\nMurdoch\ndictatorship\nwraps\n##ntal\nCorridor\nMackay\nrespectable\njewels\nunderstands\n##pathic\nBryn\n##tep\nON\ncapsule\nintrigued\nSleeping\ncommunists\n##chayat\n##current\n##vez\ndoubling\nbooklet\n##uche\nCreed\n##NU\nspies\n##sef\nadjusting\n197\nImam\nheaved\nTanya\ncanonical\nrestraint\nsenators\nstainless\n##gnate\nMatter\ncache\nrestrained\nconflicting\nstung\n##ool\nSustainable\nantiquity\n193\nheavens\ninclusive\n##ador\nfluent\n303\n911\narchaeologist\nsuperseded\n##plex\nTammy\ninspire\n##passing\n##lub\nLama\nMixing\n##activated\n##yote\nparlor\ntactic\n198\nStefano\nprostitute\nrecycling\nsorted\nbanana\nStacey\nMusée\naristocratic\ncough\n##rting\nauthorised\ngangs\nrunoff\nthoughtfully\n##nish\nFisheries\nProvence\ndetector\nhum\n##zhen\npill\n##árez\nMap\nLeaves\nPeabody\nskater\nvent\n##color\n390\ncerebral\nhostages\nmare\nJurassic\nswell\n##isans\nKnoxville\nNaked\nMalaya\nscowl\nCobra\n##anga\nSexual\n##dron\n##iae\n196\n##drick\nRavens\nBlaine\n##throp\nIsmail\nsymmetric\n##lossom\nLeicestershire\nSylvester\nglazed\n##tended\nRadar\nfused\nFamilies\nBlacks\nSale\nZion\nfoothills\nmicrowave\nslain\nCollingwood\n##pants\n##dling\nkillers\nroutinely\nJanice\nhearings\n##chanted\n##ltration\ncontinents\n##iving\n##yster\n##shot\n##yna\ninjected\nGuillaume\n##ibi\nkinda\nConfederacy\nBarnett\ndisasters\nincapable\n##grating\nrhythms\nbetting\ndraining\n##hak\nCallie\nGlover\n##iliated\nSherlock\nhearted\npunching\nWolverhampton\nLeaf\nPi\nbuilders\nfurnished\nknighted\nPhoto\n##zle\nTouring\nfumbled\npads\n##ий\nBartlett\nGunner\neerie\nMarius\nBonus\npots\n##hino\n##pta\nBray\nFrey\nOrtiz\nstalls\nbelongings\nSubway\nfascination\nmetaphor\nBat\nBoer\nColchester\nsway\n##gro\nrhetoric\n##dheim\nFool\nPMID\nadmire\n##hsil\nStrand\nTNA\n##roth\nNottinghamshire\n##mat\n##yler\nOxfordshire\n##nacle\n##roner\nBS\n##nces\nstimulus\ntransports\nSabbath\n##postle\nRichter\n4000\n##grim\n##shima\n##lette\ndeteriorated\nanalogous\n##ratic\nUHF\nenergies\ninspiring\nYiddish\nActivities\n##quential\n##boe\nMelville\n##ilton\nJudd\nconsonants\nlabs\nsmuggling\n##fari\navid\n##uc\ntruce\nundead\n##raith\nMostly\nbracelet\nConnection\nHussain\nawhile\n##UC\n##vention\nliable\ngenetically\n##phic\nImportant\nWildcats\ndaddy\ntransmit\n##cas\nconserved\nYesterday\n##lite\nNicky\nGuys\nWilder\nLay\nskinned\nCommunists\nGarfield\nNearby\norganizer\nLoss\ncrafts\nwalkway\nChocolate\nSundance\nSynod\n##enham\nmodify\nswayed\nSurface\nanalysts\nbrackets\ndrone\nparachute\nsmelling\nAndrés\nfilthy\nfrogs\nvertically\n##OK\nlocalities\nmarries\nAHL\n35th\n##pian\nPalazzo\ncube\ndismay\nrelocate\n##на\nHear\n##digo\n##oxide\nprefecture\nconverts\nhangar\n##oya\n##ucking\nSpectrum\ndeepened\nspoiled\nKeeping\n##phobic\nVerona\noutrage\nImprovement\n##UI\nmasterpiece\nslung\nCalling\nchant\nHaute\nmediated\nmanipulated\naffirmed\n##hesis\nHangul\nskies\n##llan\nWorcestershire\n##kos\nmosaic\n##bage\n##wned\nPutnam\nfolder\n##LM\nguts\nnoteworthy\n##rada\nAJ\nsculpted\n##iselle\n##rang\nrecognizable\n##pent\ndolls\nlobbying\nimpatiently\nSe\nstaple\nSerb\ntandem\nHiroshima\nthieves\n##ynx\nfaculties\nNorte\n##alle\n##trusion\nchords\n##ylon\nGareth\n##lops\n##escu\nFIA\nLevin\nauspices\ngroin\nHui\nnun\nListed\nHonourable\nLarsen\nrigorous\n##erer\nTonga\n##pment\n##rave\n##track\n##aa\n##enary\n540\nclone\nsediment\nesteem\nsighted\ncruelty\n##boa\ninverse\nviolating\nAmtrak\nStatus\namalgamated\nvertex\nAR\nharmless\nAmir\nmounts\nCoronation\ncounseling\nAudi\nCO₂\nsplits\n##eyer\nHumans\nSalmon\n##have\n##rado\n##čić\n216\ntakeoff\nclassmates\npsychedelic\n##gni\nGypsy\n231\nAnger\nGAA\nME\n##nist\n##tals\nLissa\nOdd\nbaptized\nFiat\nfringe\n##hren\n179\nelevators\nperspectives\n##TF\n##ngle\nQuestion\nfrontal\n950\nthicker\nMolecular\n##nological\nSixteen\nBaton\nHearing\ncommemorative\ndorm\nArchitectural\npurity\n##erse\nrisky\nGeorgie\nrelaxing\n##ugs\ndowned\n##rar\nSlim\n##phy\nIUCN\n##thorpe\nParkinson\n217\nMarley\nShipping\nsweaty\nJesuits\nSindh\nJanata\nimplying\nArmenians\nintercept\nAnkara\ncommissioners\nascended\nsniper\nGrass\nWalls\nsalvage\nDewey\ngeneralized\nlearnt\nPT\n##fighter\n##tech\nDR\n##itrus\n##zza\nmercenaries\nslots\n##burst\n##finger\n##nsky\nPrinces\nRhodesia\n##munication\n##strom\nFremantle\nhomework\nins\n##Os\n##hao\n##uffed\nThorpe\nXiao\nexquisite\nfirstly\nliberated\ntechnician\nOilers\nPhyllis\nherb\nsharks\nMBE\n##stock\nProduct\nbanjo\n##morandum\n##than\nVisitors\nunavailable\nunpublished\noxidation\nVogue\n##copic\n##etics\nYates\n##ppard\nLeiden\nTrading\ncottages\nPrinciples\n##Millan\n##wife\n##hiva\nVicar\nnouns\nstrolled\n##eorological\n##eton\n##science\nprecedent\nArmand\nGuido\nrewards\n##ilis\n##tise\nclipped\nchick\n##endra\naverages\ntentatively\n1830s\n##vos\nCertainly\n305\nSociété\nCommandant\n##crats\n##dified\n##nka\nmarsh\nangered\nventilation\nHutton\nRitchie\n##having\nEclipse\nflick\nmotionless\nAmor\nFest\nLoire\nlays\n##icit\n##sband\nGuggenheim\nLuck\ndisrupted\n##ncia\nDisco\n##vigator\ncriticisms\ngrins\n##lons\n##vial\n##ody\nsalute\nCoaches\njunk\nsaxophonist\n##eology\nUprising\nDiet\n##marks\nchronicles\nrobbed\n##iet\n##ahi\nBohemian\nmagician\nwavelength\nKenyan\naugmented\nfashionable\n##ogies\nLuce\nF1\nMonmouth\n##jos\n##loop\nenjoyment\nexemption\nCenters\n##visor\nSoundtrack\nblinding\npractitioner\nsolidarity\nsacrificed\n##oso\n##cture\n##riated\nblended\nAbd\nCopyright\n##nob\n34th\n##reak\nClaudio\nhectare\nrotor\ntestify\n##ends\n##iably\n##sume\nlandowner\n##cess\n##ckman\nEduard\nSilesian\nbackseat\nmutually\n##abe\nMallory\nbounds\nCollective\nPoet\nWinkler\npertaining\nscraped\nPhelps\ncrane\nflickering\nProto\nbubbles\npopularized\nremoves\n##86\nCadillac\nWarfare\naudible\nrites\nshivering\n##sist\n##nst\n##biotic\nMon\nfascist\nBali\nKathryn\nambiguous\nfuriously\nmorale\npatio\nSang\ninconsistent\ntopology\nGreens\nmonkeys\nKöppen\n189\nToy\nvow\n##ías\nbombings\n##culus\nimprovised\nlodged\nsubsidiaries\ngarment\nstartling\npractised\nHume\nThorn\ncategorized\nTill\nEileen\nwedge\n##64\nFederico\npatriotic\nunlock\n##oshi\nbadminton\nCompared\nVilnius\n##KE\nCrimean\nKemp\ndecks\nspaced\nresolutions\nsighs\n##mind\nImagine\nCartoon\nhuddled\npolicemen\nforwards\n##rouch\nequals\n##nter\ninspected\nCharley\nMG\n##rte\npamphlet\nArturo\ndans\nscarcely\n##ulton\n##rvin\nparental\nunconstitutional\nwatts\nSusannah\nDare\n##sitive\nRowland\nValle\ninvalid\n##ué\nDetachment\nacronym\nYokohama\nverified\n##ls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##raw\nMacKenzie\nsu\n##ienced\n##iggs\n##quitaine\nbisexual\n##noon\nrunways\nsubsp\n##!\n##\"\n###\n##$\n##%\n##&\n##'\n##(\n##)\n##*\n##+\n##,\n##-\n##.\n##/\n##:\n##;\n##<\n##=\n##>\n##?\n##@\n##[\n##\\\n##]\n##^\n##_\n##`\n##{\n##|\n##}\n##~\n##¡\n##¢\n##£\n##¥\n##§\n##¨\n##©\n##ª\n##«\n##¬\n##®\n##±\n##´\n##µ\n##¶\n##·\n##¹\n##º\n##»\n##¼\n##¾\n##¿\n##À\n##Á\n##Â\n##Ä\n##Å\n##Æ\n##Ç\n##È\n##É\n##Í\n##Î\n##Ñ\n##Ó\n##Ö\n##×\n##Ø\n##Ú\n##Ü\n##Þ\n##â\n##ã\n##æ\n##ç\n##î\n##ï\n##ð\n##ñ\n##ô\n##õ\n##÷\n##û\n##þ\n##ÿ\n##Ā\n##ą\n##Ć\n##Č\n##ď\n##Đ\n##đ\n##ē\n##ė\n##ę\n##ě\n##ğ\n##ġ\n##Ħ\n##ħ\n##ĩ\n##Ī\n##İ\n##ļ\n##Ľ\n##ľ\n##Ł\n##ņ\n##ň\n##ŋ\n##Ō\n##ŏ\n##ő\n##Œ\n##œ\n##ř\n##Ś\n##ś\n##Ş\n##Š\n##Ţ\n##ţ\n##ť\n##ũ\n##ŭ\n##ů\n##ű\n##ų\n##ŵ\n##ŷ\n##ź\n##Ż\n##ż\n##Ž\n##ž\n##Ə\n##ƒ\n##ơ\n##ư\n##ǎ\n##ǐ\n##ǒ\n##ǔ\n##ǫ\n##Ș\n##Ț\n##ț\n##ɐ\n##ɑ\n##ɔ\n##ɕ\n##ə\n##ɛ\n##ɡ\n##ɣ\n##ɨ\n##ɪ\n##ɲ\n##ɾ\n##ʀ\n##ʁ\n##ʂ\n##ʃ\n##ʊ\n##ʋ\n##ʌ\n##ʐ\n##ʑ\n##ʒ\n##ʔ\n##ʰ\n##ʲ\n##ʳ\n##ʷ\n##ʻ\n##ʼ\n##ʾ\n##ʿ\n##ˈ\n##ː\n##ˡ\n##ˢ\n##ˣ\n##́\n##̃\n##̍\n##̯\n##͡\n##Α\n##Β\n##Γ\n##Δ\n##Ε\n##Η\n##Θ\n##Ι\n##Κ\n##Λ\n##Μ\n##Ν\n##Ο\n##Π\n##Σ\n##Τ\n##Φ\n##Χ\n##Ψ\n##Ω\n##ά\n##έ\n##ή\n##ί\n##β\n##γ\n##δ\n##ε\n##ζ\n##η\n##θ\n##ι\n##κ\n##λ\n##μ\n##ξ\n##ο\n##π\n##ρ\n##σ\n##τ\n##υ\n##φ\n##χ\n##ψ\n##ω\n##ό\n##ύ\n##ώ\n##І\n##Ј\n##А\n##Б\n##В\n##Г\n##Д\n##Е\n##Ж\n##З\n##И\n##К\n##Л\n##М\n##Н\n##О\n##П\n##Р\n##С\n##Т\n##У\n##Ф\n##Х\n##Ц\n##Ч\n##Ш\n##Э\n##Ю\n##Я\n##б\n##в\n##г\n##д\n##ж\n##з\n##к\n##л\n##м\n##п\n##с\n##т\n##у\n##ф\n##х\n##ц\n##ч\n##ш\n##щ\n##ъ\n##ы\n##ь\n##э\n##ю\n##ё\n##і\n##ї\n##ј\n##њ\n##ћ\n##Ա\n##Հ\n##ա\n##ե\n##ի\n##կ\n##մ\n##յ\n##ն\n##ո\n##ս\n##տ\n##ր\n##ւ\n##ְ\n##ִ\n##ֵ\n##ֶ\n##ַ\n##ָ\n##ֹ\n##ּ\n##א\n##ב\n##ג\n##ד\n##ה\n##ו\n##ז\n##ח\n##ט\n##י\n##כ\n##ל\n##ם\n##מ\n##ן\n##נ\n##ס\n##ע\n##פ\n##צ\n##ק\n##ר\n##ש\n##ת\n##،\n##ء\n##آ\n##أ\n##إ\n##ئ\n##ا\n##ب\n##ت\n##ث\n##ج\n##ح\n##خ\n##ذ\n##ز\n##س\n##ش\n##ص\n##ض\n##ط\n##ظ\n##ع\n##غ\n##ف\n##ق\n##ك\n##ل\n##و\n##ى\n##َ\n##ِ\n##ٹ\n##پ\n##چ\n##ک\n##گ\n##ہ\n##ی\n##ے\n##ं\n##आ\n##क\n##ग\n##च\n##ज\n##ण\n##त\n##द\n##ध\n##न\n##प\n##ब\n##भ\n##म\n##य\n##र\n##ल\n##व\n##श\n##ष\n##स\n##ह\n##ा\n##ि\n##ी\n##ु\n##े\n##ो\n##्\n##।\n##॥\n##আ\n##ই\n##এ\n##ও\n##ক\n##খ\n##গ\n##চ\n##ছ\n##জ\n##ট\n##ত\n##থ\n##দ\n##ধ\n##ন\n##প\n##ব\n##ম\n##য\n##র\n##ল\n##শ\n##স\n##হ\n##়\n##া\n##ি\n##ী\n##ু\n##ে\n##ো\n##্\n##য়\n##க\n##த\n##ப\n##ம\n##ய\n##ர\n##ல\n##வ\n##ா\n##ி\n##ு\n##்\n##ร\n##་\n##ག\n##ང\n##ད\n##ན\n##བ\n##མ\n##ར\n##ལ\n##ས\n##ི\n##ུ\n##ེ\n##ོ\n##ა\n##ე\n##ი\n##ლ\n##ნ\n##ო\n##რ\n##ს\n##ᴬ\n##ᴵ\n##ᵀ\n##ᵃ\n##ᵇ\n##ᵈ\n##ᵉ\n##ᵍ\n##ᵏ\n##ᵐ\n##ᵒ\n##ᵖ\n##ᵗ\n##ᵘ\n##ᵣ\n##ᵤ\n##ᵥ\n##ᶜ\n##ᶠ\n##ḍ\n##Ḥ\n##ḥ\n##Ḩ\n##ḩ\n##ḳ\n##ṃ\n##ṅ\n##ṇ\n##ṛ\n##ṣ\n##ṭ\n##ạ\n##ả\n##ấ\n##ầ\n##ẩ\n##ậ\n##ắ\n##ế\n##ề\n##ể\n##ễ\n##ệ\n##ị\n##ọ\n##ố\n##ồ\n##ổ\n##ộ\n##ớ\n##ờ\n##ợ\n##ụ\n##ủ\n##ứ\n##ừ\n##ử\n##ữ\n##ự\n##ỳ\n##ỹ\n##ἀ\n##ἐ\n##ὁ\n##ὐ\n##ὰ\n##ὶ\n##ὸ\n##ῆ\n##ῖ\n##ῦ\n##ῶ\n##‐\n##‑\n##‒\n##–\n##—\n##―\n##‖\n##‘\n##’\n##‚\n##“\n##”\n##„\n##†\n##‡\n##•\n##…\n##‰\n##′\n##″\n##⁄\n##⁰\n##ⁱ\n##⁴\n##⁵\n##⁶\n##⁷\n##⁸\n##⁹\n##⁻\n##ⁿ\n##₅\n##₆\n##₇\n##₈\n##₉\n##₊\n##₍\n##₎\n##ₐ\n##ₑ\n##ₒ\n##ₓ\n##ₕ\n##ₖ\n##ₘ\n##ₚ\n##ₛ\n##ₜ\n##₤\n##€\n##₱\n##₹\n##ℓ\n##№\n##ℝ\n##⅓\n##←\n##↑\n##→\n##↔\n##⇌\n##⇒\n##∂\n##∈\n##∗\n##∘\n##√\n##∞\n##∧\n##∨\n##∩\n##∪\n##≈\n##≠\n##≡\n##≤\n##≥\n##⊂\n##⊆\n##⊕\n##⋅\n##─\n##│\n##■\n##●\n##★\n##☆\n##☉\n##♠\n##♣\n##♥\n##♦\n##♯\n##⟨\n##⟩\n##ⱼ\n##、\n##。\n##《\n##》\n##「\n##」\n##『\n##』\n##〜\n##い\n##う\n##え\n##お\n##か\n##き\n##く\n##け\n##こ\n##さ\n##し\n##す\n##せ\n##そ\n##た\n##ち\n##つ\n##て\n##と\n##な\n##に\n##の\n##は\n##ひ\n##ま\n##み\n##む\n##め\n##も\n##や\n##ゆ\n##よ\n##ら\n##り\n##る\n##れ\n##ん\n##ア\n##ィ\n##イ\n##ウ\n##エ\n##オ\n##カ\n##ガ\n##キ\n##ク\n##グ\n##コ\n##サ\n##シ\n##ジ\n##ス\n##ズ\n##タ\n##ダ\n##ッ\n##テ\n##デ\n##ト\n##ド\n##ナ\n##ニ\n##ハ\n##バ\n##パ\n##フ\n##ブ\n##プ\n##マ\n##ミ\n##ム\n##ャ\n##ュ\n##ラ\n##リ\n##ル\n##レ\n##ロ\n##ン\n##・\n##ー\n##一\n##三\n##上\n##下\n##中\n##事\n##二\n##井\n##京\n##人\n##亻\n##仁\n##佐\n##侍\n##光\n##公\n##力\n##北\n##十\n##南\n##原\n##口\n##史\n##司\n##吉\n##同\n##和\n##囗\n##国\n##國\n##土\n##城\n##士\n##大\n##天\n##太\n##夫\n##女\n##子\n##宀\n##安\n##宮\n##宿\n##小\n##尚\n##山\n##島\n##川\n##州\n##平\n##年\n##心\n##愛\n##戸\n##文\n##新\n##方\n##日\n##明\n##星\n##書\n##月\n##木\n##本\n##李\n##村\n##東\n##松\n##林\n##正\n##武\n##氏\n##水\n##氵\n##江\n##河\n##海\n##版\n##犬\n##王\n##生\n##田\n##白\n##皇\n##省\n##真\n##石\n##社\n##神\n##竹\n##美\n##義\n##花\n##藤\n##西\n##谷\n##車\n##辶\n##道\n##郎\n##郡\n##部\n##野\n##金\n##長\n##門\n##陽\n##青\n##食\n##馬\n##高\n##龍\n##龸\n##사\n##씨\n##의\n##이\n##한\n##fi\n##fl\n##！\n##（\n##）\n##，\n##－\n##／\n##：\n"
},
{
"path": "func_builders/input_fn_builder.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description: \n# \n# \n\n\nimport tensorflow as tf\n\n\ndef file_based_input_fn_builder(input_file, num_window=None, window_size=None, max_num_mention=None, is_training=False, drop_remainder=True):\n \"\"\"Creates an `input_fn` closure to be passed to TPUEstimator.\"\"\"\n name_to_features = {\n 'sentence_map': tf.FixedLenFeature([num_window * window_size], tf.int64),\n 'text_len': tf.FixedLenFeature([num_window], tf.int64),\n 'subtoken_map': tf.FixedLenFeature([num_window * window_size], tf.int64),\n 'speaker_ids': tf.FixedLenFeature([num_window * window_size], tf.int64),\n 'flattened_input_ids': tf.FixedLenFeature([num_window * window_size], tf.int64),\n 'flattened_input_mask': tf.FixedLenFeature([num_window * window_size], tf.int64),\n 'span_starts': tf.FixedLenFeature([max_num_mention], tf.int64),\n 'span_ends': tf.FixedLenFeature([max_num_mention], tf.int64), \n 'cluster_ids': tf.FixedLenFeature([max_num_mention], tf.int64),\n }\n\n\n def _decode_record(record, name_to_features):\n \"\"\"Decodes a record to a TensorFlow example.\"\"\"\n example = tf.io.parse_single_example(record, name_to_features)\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t \n return example\n\n\n def input_fn_from_tfrecord(params):\n \"\"\"The actual input function.\"\"\"\n batch_size = params[\"batch_size\"]\n\n # For training, we want a lot of parallel reading and shuffling.\n # For eval, we want no shuffling and parallel reading doesn't matter.\n d = tf.data.TFRecordDataset(input_file)\n if is_training:\n d = d.repeat() \n d = d.shuffle(buffer_size=100)\n \n d = d.apply(\n tf.contrib.data.map_and_batch(\n lambda record: _decode_record(record, name_to_features),\n batch_size=batch_size,\n drop_remainder=drop_remainder))\n\n return d\n \n return input_fn_from_tfrecord "
},
{
"path": "func_builders/model_fn_builder.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# \n#\n\n\n\nimport tensorflow as tf\nfrom utils import util\nfrom utils.radam import RAdam\n\n\ndef model_fn_builder(config, model_sign=\"mention_proposal\"):\n\n def mention_proposal_model_fn(features, labels, mode, params): \n \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n input_ids = features[\"flattened_input_ids\"]\n input_mask = features[\"flattened_input_mask\"]\n text_len = features[\"text_len\"]\n speaker_ids = features[\"speaker_ids\"]\n gold_starts = features[\"span_starts\"]\n gold_ends = features[\"span_ends\"]\n cluster_ids = features[\"cluster_ids\"]\n sentence_map = features[\"sentence_map\"] \n \n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n model = util.get_model(config, model_sign=\"mention_proposal\")\n\n if config.use_tpu:\n def tpu_scaffold():\n return tf.train.Scaffold()\n scaffold_fn = tpu_scaffold\n else:\n scaffold_fn = None \n\n if mode == tf.estimator.ModeKeys.TRAIN: \n tf.logging.info(\"****************************** tf.estimator.ModeKeys.TRAIN ******************************\")\n tf.logging.info(\"********* Features *********\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n instance = (input_ids, input_mask, sentence_map, text_len, speaker_ids, gold_starts, gold_ends, cluster_ids)\n total_loss, start_scores, end_scores, span_scores = model.get_mention_proposal_and_loss(instance, is_training)\n gold_start_sequence_labels, gold_end_sequence_labels, gold_span_sequence_labels = model.get_gold_mention_sequence_labels_from_pad_index(gold_starts, gold_ends, text_len)\n\n if config.use_tpu:\n optimizer = tf.train.AdamOptimizer(learning_rate=config.learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08)\n optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)\n train_op = optimizer.minimize(total_loss, tf.train.get_global_step()) \n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn)\n else:\n optimizer = RAdam(learning_rate=config.learning_rate, epsilon=1e-8, beta1=0.9, beta2=0.999)\n train_op = optimizer.minimize(total_loss, tf.train.get_global_step())\n \n train_logging_hook = tf.train.LoggingTensorHook({\"loss\": total_loss}, every_n_iter=1)\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn,\n training_hooks=[train_logging_hook])\n\n elif mode == tf.estimator.ModeKeys.EVAL: \n tf.logging.info(\"****************************** tf.estimator.ModeKeys.EVAL ******************************\")\n \n instance = (input_ids, input_mask, sentence_map, text_len, speaker_ids, gold_starts, gold_ends, cluster_ids)\n total_loss, start_scores, end_scores, span_scores = model.get_mention_proposal_and_loss(instance, is_training)\n total_loss, start_scores, end_scores, span_scores = model.get_mention_proposal_and_loss(instance, is_training)\n gold_start_sequence_labels, gold_end_sequence_labels, gold_span_sequence_labels = model.get_gold_mention_sequence_labels_from_pad_index(gold_starts, gold_ends, text_len)\n\n def metric_fn(start_scores, end_scores, span_scores, gold_span_label):\n start_scores = tf.reshape(start_scores, [-1, config.window_size])\n end_scores = tf.reshape(end_scores, [-1, config.window_size])\n start_scores = tf.tile(tf.expand_dims(start_scores, 2), [1, 1, config.window_size])\n end_scores = tf.tile(tf.expand_dims(end_scores, 2), [1, 1, config.window_size])\n sce_span_scores = (start_scores + end_scores + span_scores)/ 3\n pred_span_label = tf.cast(tf.reshape(tf.math.greater_equal(sce_span_scores, config.mention_threshold), [-1]), tf.bool)\n\n gold_span_label = tf.cast(tf.reshape(gold_span_sequence_labels, [-1]), tf.bool)\n\n return {\"precision\": tf.compat.v1.metrics.precision(gold_span_label, pred_span_label), \n \"recall\": tf.compat.v1.metrics.recall(gold_span_label, pred_span_label)}\n\n eval_metrics = (metric_fn, [start_scores, end_scores, span_scores])\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=tf.estimator.ModeKeys.EVAL,\n loss=total_loss,\n eval_metrics=eval_metrics,\n scaffold_fn=scaffold_fn)\n\n elif mode == tf.estimator.ModeKeys.PREDICT:\n tf.logging.info(\"****************************** tf.estimator.ModeKeys.PREDICT ******************************\")\n \n instance = (input_ids, input_mask, sentence_map, text_len, speaker_ids, gold_starts, gold_ends, cluster_ids)\n total_loss, start_scores, end_scores, span_scores = model.get_mention_proposal_and_loss(instance, is_training)\n gold_start_sequence_labels, gold_end_sequence_labels, gold_span_sequence_labels = model.get_gold_mention_sequence_labels_from_pad_index(gold_starts, gold_ends, text_len)\n predictions = {\n \"total_loss\": total_loss,\n \"start_scores\": start_scores,\n \"start_gold\": gold_starts,\n \"end_gold\": gold_ends,\n \"end_scores\": end_scores, \n \"span_scores\": span_scores\n } \n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=tf.estimator.ModeKeys.PREDICT,\n predictions=predictions,\n scaffold_fn=scaffold_fn)\n else:\n raise ValueError(\"Please check the the mode ! \")\n \n return output_spec\n\n\n def corefqa_model_fn(features, labels, mode, params):\n\n \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n input_ids = features[\"flattened_input_ids\"]\n input_mask = features[\"flattened_input_mask\"]\n text_len = features[\"text_len\"]\n speaker_ids = features[\"speaker_ids\"]\n gold_starts = features[\"span_starts\"]\n gold_ends = features[\"span_ends\"]\n cluster_ids = features[\"cluster_ids\"]\n sentence_map = features[\"sentence_map\"] \n \n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n model = util.get_model(config, model_sign=\"corefqa\")\n \n if config.use_tpu:\n tf.logging.info(\"****************************** Training on TPU ******************************\")\n def tpu_scaffold():\n return tf.train.Scaffold()\n scaffold_fn = tpu_scaffold\n else:\n scaffold_fn = None \n\n\n if mode == tf.estimator.ModeKeys.TRAIN:\n tf.logging.info(\"****************************** tf.estimator.ModeKeys.TRAIN ******************************\")\n tf.logging.info(\"********* Features *********\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n instance = (input_ids, input_mask, sentence_map, text_len, speaker_ids, gold_starts, gold_ends, cluster_ids)\n total_loss, (topk_mention_start_indices, topk_mention_end_indices), (forward_topc_mention_start_indices, forward_topc_mention_end_indices), top_mention_span_linking_scores = model.get_coreference_resolution_and_loss(instance, is_training, use_tpu=config.use_tpu)\n\n if config.use_tpu:\n optimizer = tf.train.AdamOptimizer(learning_rate=config.learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08)\n optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)\n train_op = optimizer.minimize(total_loss, tf.train.get_global_step()) \n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=tf.estimator.ModeKeys.TRAIN,\n loss=total_loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn)\n else:\n optimizer = RAdam(learning_rate=config.learning_rate, epsilon=1e-8, beta1=0.9, beta2=0.999)\n train_op = optimizer.minimize(total_loss, tf.train.get_global_step())\n\n training_logging_hook = tf.train.LoggingTensorHook({\"loss\": total_loss}, every_n_iter=1)\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=tf.estimator.ModeKeys.TRAIN,\n loss=total_loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn, \n training_hooks=[training_logging_hook])\n\n\n elif mode == tf.estimator.ModeKeys.EVAL: \n tf.logging.info(\"****************************** tf.estimator.ModeKeys.EVAL ******************************\")\n tf.logging.info(\"@@@@@ MERELY support tf.estimator.ModeKeys.PREDICT ! @@@@@\")\n tf.logging.info(\"@@@@@ YOU can EVAL your checkpoints after the training process. @@@@@\") \n tf.logging.info(\"****************************** tf.estimator.ModeKeys.EVAL ******************************\")\n \n elif mode == tf.estimator.ModeKeys.PREDICT :\n tf.logging.info(\"****************************** tf.estimator.ModeKeys.PREDICT ******************************\")\n\n instance = (input_ids, input_mask, sentence_map, text_len, speaker_ids, gold_starts, gold_ends, cluster_ids)\n total_loss, (topk_mention_start_indices, topk_mention_end_indices), (forward_topc_mention_start_indices, forward_topc_mention_end_indices), top_mention_span_linking_scores = model.get_coreference_resolution_and_loss(instance, True, use_tpu=config.use_tpu)\n\n top_antecedent = tf.math.argmax(top_mention_span_linking_scores, axis=-1)\n predictions = {\n \"total_loss\": total_loss, \n \"topk_span_starts\": topk_mention_start_indices,\n \"topk_span_ends\": topk_mention_end_indices, \n \"top_antecedent_scores\": top_mention_span_linking_scores,\n \"top_antecedent\": top_antecedent,\n \"cluster_ids\" : cluster_ids, \n \"gold_starts\": gold_starts, \n \"gold_ends\": gold_ends} \n\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(mode=tf.estimator.ModeKeys.PREDICT, \n predictions=predictions, \n scaffold_fn=scaffold_fn)\n else:\n raise ValueError(\"Please check the the mode ! \")\n return output_spec\n\n\n if model_sign == \"mention_proposal\":\n return mention_proposal_model_fn\n elif model_sign == \"corefqa\":\n return corefqa_model_fn\n else:\n raise ValueError(\"Please check the model sign! Only support [mention_proposal] and [corefqa] .\")\n\n\n\n\n\n\n\n"
},
{
"path": "logs/corefqa_log.txt",
"content": "/home/xiaoyli1110/venv/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n from ._conv import register_converters as _register_converters\n/home/xiaoyli1110/xiaoya/Coref-tf\nW0713 13:36:28.637916 139854454523648 module_wrapper.py:139] From /home/xiaoyli1110/xiaoya/Coref-tf/run/train_corefqa.py:308: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n\nloading experiments_tpu.conf ... \nW0713 13:36:28.752999 139854454523648 module_wrapper.py:139] From /home/xiaoyli1110/xiaoya/Coref-tf/utils/util.py:41: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\nI0716 13:36:28.753216 139854454523648 util.py:41] %*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%\nI0716 13:36:28.753291 139854454523648 util.py:42] %*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%\nI0716 13:36:28.753346 139854454523648 util.py:43] %%%%%%%% Configs are showed as follows : %%%%%%%%\nI0716 13:36:28.753432 139854454523648 util.py:45] max_top_antecedents : 60\nI0716 13:36:28.753505 139854454523648 util.py:45] max_training_sentences : 3\nI0716 13:36:28.753576 139854454523648 util.py:45] top_span_ratio : 0.3\nI0716 13:36:28.753644 139854454523648 util.py:45] max_num_speakers : 20\nI0716 13:36:28.753710 139854454523648 util.py:45] max_segment_len : 384\nI0716 13:36:28.753773 139854454523648 util.py:45] max_cluster_num : 30\nI0716 13:36:28.753848 139854454523648 util.py:45] tpu : True\nI0716 13:36:28.753910 139854454523648 util.py:45] max_query_len : 150\nI0716 13:36:28.753972 139854454523648 util.py:45] max_context_len : 150\nI0716 13:36:28.754034 139854454523648 util.py:45] max_qa_len : 300\nI0716 13:36:28.754097 139854454523648 util.py:45] hidden_size : 1024\nI0716 13:36:28.754161 139854454523648 util.py:45] max_candidate_mentions : 60\nI0716 13:36:28.754229 139854454523648 util.py:45] learning_rate : 8e-06\nI0716 13:36:28.754301 139854454523648 util.py:45] num_docs : 5604\nI0716 13:36:28.754365 139854454523648 util.py:45] start_ratio : 0.8\nI0716 13:36:28.754428 139854454523648 util.py:45] end_ratio : 0.8\nI0716 13:36:28.754492 139854454523648 util.py:45] mention_ratio : 1.0\nI0716 13:36:28.754556 139854454523648 util.py:45] corefqa_loss_ratio : 0.9\nI0716 13:36:28.754620 139854454523648 util.py:45] score_ratio : 0.5\nI0716 13:36:28.754682 139854454523648 util.py:45] run : estimator\nI0716 13:36:28.754746 139854454523648 util.py:45] threshold : 0.5\nI0716 13:36:28.754814 139854454523648 util.py:45] dropout_rate : 0.3\nI0716 13:36:28.754945 139854454523648 util.py:45] ffnn_size : 1024\nI0716 13:36:28.755008 139854454523648 util.py:45] ffnn_depth : 1\nI0716 13:36:28.755071 139854454523648 util.py:45] num_epochs : 8\nI0716 13:36:28.755135 139854454523648 util.py:45] max_span_width : 30\nI0716 13:36:28.755199 139854454523648 util.py:45] use_segment_distance : True\nI0716 13:36:28.755261 139854454523648 util.py:45] model_heads : True\nI0716 13:36:28.755324 139854454523648 util.py:45] coref_depth : 2\nI0716 13:36:28.755383 139854454523648 util.py:45] corefqa_only_concate : False\nI0716 13:36:28.755445 139854454523648 util.py:45] train_path : gs://xiaoy-data-europe/overlap_384_3/train.128.english.tfrecord\nI0716 13:36:28.755507 139854454523648 util.py:45] eval_path : test.english.jsonlines\nI0716 13:36:28.755571 139854454523648 util.py:45] conll_eval_path : gs://corefqa-europe/spanbert_large_overlap_384_3_out\nput_2e-5/test.english.v4_gold_conll\nI0716 13:36:28.755634 139854454523648 util.py:45] single_example : False\nI0716 13:36:28.755702 139854454523648 util.py:45] genres : ['bc', 'bn', 'mz', 'nw', 'pt', 'tc', 'wb']\nI0716 13:36:28.755765 139854454523648 util.py:45] log_root : gs://corefqa-europe/spanbert_large_overlap_384_3_output_2e-5\nI0716 13:36:28.755842 139854454523648 util.py:45] save_checkpoints_steps : 1000\nI0716 13:36:28.755906 139854454523648 util.py:45] dev_path : gs://xiaoy-data-europe/overlap_384_3/dev.256.english.tfrecord\nI0716 13:36:28.755968 139854454523648 util.py:45] test_path : gs://xiaoy-data-europe/overlap_384_3/test.256.english.tfrecord\nI0716 13:36:28.756030 139854454523648 util.py:45] bert_config_file : gs://xiaoy-data-europe/spanbert_large_tf/bert_config.json\nI0716 13:36:28.756093 139854454523648 util.py:45] vocab_file : gs://xiaoy-data-europe/spanbert_large_tf/vocab.txt\nI0716 13:36:28.756155 139854454523648 util.py:45] tf_checkpoint : gs://xiaoy-data-europe/spanbert_large_tf/bert_model.ckpt\nI0716 13:36:28.756217 139854454523648 util.py:45] init_checkpoint : gs://xiaoy-data-europe/spanbert_large_tf/bert_model.ckpt\nI0716 13:36:28.756279 139854454523648 util.py:45] eval_checkpoint : gs://corefqa-europe/spanbert_large_overlap_384_3_out\nput_1e-5_0.3_8/model.ckpt-20\nI0716 13:36:28.756341 139854454523648 util.py:45] output_path : gs://corefqa-europe/spanbert_large_overlap_384_3_output_\n1e-5_0.3_8\nI0716 13:36:28.756391 139854454523648 util.py:47] %*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%%*%\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is ***** \nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.6219, recall: 0.5093, f1: 0.56\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.6413, recall: 0.5428, f1: 0.588\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-1000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.6227, recall: 0.5841, f1: 0.6028\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.6744, recall: 0.6126, f1: 0.6149\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-1500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.6906, recall: 0.5288, f1: 0.599\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.6966, recall: 0.506, f1: 0.5862\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-2000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.6712, recall: 0.5717, f1: 0.6174\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.6696, recall: 0.541, f1: 0.5985\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-2500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8091, recall: 0.5967, f1: 0.6868\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.8018, recall: 0.5692, f1: 0.6657\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-3000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.6675, recall: 0.6382, f1: 0.6525\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-3500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.7155, recall: 0.7515, f1: 0.7331\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.7239, recall: 0.7711, f1: 0.7468\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-4000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.7762, recall: 0.5819, f1: 0.6651\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-4500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.6661, recall: 0.6236, f1: 0.6442\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-5000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.7814, recall: 0.7246, f1: 0.7519\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.7155, recall: 0.7515, f1: 0.7331\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-5500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8042, recall: 0.7417, f1: 0.7717\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.8439, recall: 0.6328, f1: 0.7232\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-6000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.7942, recall: 0.7217, f1: 0.7417\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-6500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.7816, recall: 0.7831, f1: 0.7823\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.7893, recall: 0.8075, f1: 0.7983\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-7000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8103, recall: 0.814, f1: 0.8121\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.8022, recall: 0.7838, f1: 0.7929\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-7500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.7935, recall: 0.8292, f1: 0.8109\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-8000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8997, recall: 0.7292, f1: 0.8056\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-8500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8737, recall: 0.7444, f1: 0.8039\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-9000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8107, recall: 0.814, f1: 0.8124\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-10500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8439, recall: 0.7952, f1: 0.8188\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.8228, recall: 0.8093, f1: 0.8107\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-11000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8386, recall: 0.8147, f1: 0.8273\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.8239, recall: 0.8104, f1: 0.8143\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-12000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8398, recall: 0.8201, f1: 0.8369\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-12500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8666, recall: 0.7766, f1: 0.8192\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-13000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8397, recall: 0.7892, f1: 0.8056\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-13500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8357, recall: 0.8169, f1: 0.8262\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON TEST SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [TEST EVAL] ***** : precision: 0.8327, recall: 0.8288, f1: 0.8322\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-14000\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8269, recall: 0.8108, f1: 0.8201\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-14500\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:17:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8344, recall: 0.8104, f1: 0.8215\nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] ***** Current ckpt path is *****\nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-15000\nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] ***** EVAL ON DEV SET *****\nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] ***** [DEV EVAL] ***** : precision: 0.8287, recall: 0.8177, f1: 0.8223\nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] *************************\nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] - @@@@@ the path to the BEST DEV result is : gs://corefqa-europe-europe/spanbert_large_overlap_384_3_output_8e6_0.2_8/model.ckpt-13500 \nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] - @@@@@ BEST DEV F1 : 0.8262, Precision : 0.8357, Recall : 0.8169\nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] - @@@@@ TEST when DEV best F1 : 0.8322, Precision : 0.8327, Recall : 0.8288 \nI0716 14:46:19.214575 139854454523648 tpu_estimator.py:2307] - @@@@@ mention_proposal_only_concate False"
},
{
"path": "models/corefqa.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\nimport os\nimport sys \n\nrepo_path = \"/\".join(os.path.realpath(__file__).split(\"/\")[:-2])\nif repo_path not in sys.path:\n sys.path.insert(0, repo_path)\n\nimport tensorflow as tf\nfrom bert import modeling\n\n\n\nclass CorefQAModel(object):\n def __init__(self, config):\n self.config = config \n self.dropout = None\n self.pad_idx = 0 \n self.mention_start_idx = 37\n self.mention_end_idx = 42\n self.bert_config = modeling.BertConfig.from_json_file(config.bert_config_file)\n self.bert_config.hidden_dropout_prob = config.dropout_rate\n self.cls_in_vocab = 101\n self.sep_in_vocab = 102\n\n\n def get_coreference_resolution_and_loss(self, instance, is_training, use_tpu=False):\n\n\n self.use_tpu = use_tpu \n self.dropout = self.get_dropout(self.config.dropout_rate, is_training)\n\n flat_window_input_ids, flat_window_input_mask, flat_doc_sentence_map, window_text_len, speaker_ids, gold_starts, gold_ends, gold_cluster_ids = instance\n # flat_input_ids: (num_window, window_size)\n # flat_doc_overlap_input_mask: (num_window, window_size)\n # flat_sentence_map: (num_window, window_size)\n # text_len: dynamic length and is padded to fix length\n # gold_start: (max_num_mention), mention start index in the original (NON-OVERLAP) document. Pad with -1 to the fix length max_num_mention.\n # gold_end: (max_num_mention), mention end index in the original (NON-OVERLAP) document. Pad with -1 to the fix length max_num_mention.\n # cluster_ids/speaker_ids is not used in the mention proposal model.\n\n flat_window_input_ids = tf.math.maximum(flat_window_input_ids, tf.zeros_like(flat_window_input_ids, tf.int32)) # (num_window * window_size)\n \n flat_doc_overlap_input_mask = tf.where(tf.math.greater_equal(flat_window_input_mask, 0), \n x=tf.ones_like(flat_window_input_mask, tf.int32), y=tf.zeros_like(flat_window_input_mask, tf.int32)) # (num_window * window_size)\n # flat_doc_overlap_input_mask = tf.math.maximum(flat_doc_overlap_input_mask, tf.zeros_like(flat_doc_overlap_input_mask, tf.int32))\n flat_doc_sentence_map = tf.math.maximum(flat_doc_sentence_map, tf.zeros_like(flat_doc_sentence_map, tf.int32)) # (num_window * window_size)\n \n gold_start_end_mask = tf.cast(tf.math.greater_equal(gold_starts, tf.zeros_like(gold_starts, tf.int32)), tf.bool) # (max_num_mention)\n gold_start_index_labels = self.boolean_mask_1d(gold_starts, gold_start_end_mask, name_scope=\"gold_starts\", use_tpu=self.use_tpu) # (num_of_mention)\n gold_end_index_labels = self.boolean_mask_1d(gold_ends, gold_start_end_mask, name_scope=\"gold_ends\", use_tpu=self.use_tpu) # (num_of_mention)\n\n gold_cluster_mask = tf.cast(tf.math.greater_equal(gold_cluster_ids, tf.zeros_like(gold_cluster_ids, tf.int32)), tf.bool) # (max_num_cluster)\n gold_cluster_ids = self.boolean_mask_1d(gold_cluster_ids, gold_cluster_mask, name_scope=\"gold_cluster\", use_tpu=self.use_tpu)\n\n window_text_len = tf.math.maximum(window_text_len, tf.zeros_like(window_text_len, tf.int32)) # (num_of_non_empty_window)\n num_subtoken_in_doc = tf.math.reduce_sum(window_text_len) # the value should be num_subtoken_in_doc \n ####################\n ####################\n ## mention proposal stage starts \n mention_input_ids = tf.reshape(flat_window_input_ids, [-1, self.config.window_size]) # (num_window, window_size)\n # each row of mention_input_ids is a subdocument \n mention_input_mask = tf.ones_like(mention_input_ids, tf.int32) # (num_window, window_size)\n mention_model = modeling.BertModel(config=self.bert_config, is_training=is_training, \n input_ids=mention_input_ids, input_mask=mention_input_mask, use_one_hot_embeddings=False, scope='bert')\n\n mention_doc_overlap_window_embs = mention_model.get_sequence_output() # (num_window, window_size, hidden_size)\n # get BERT embeddings for mention_input_ids \n doc_overlap_input_mask = tf.reshape(flat_doc_overlap_input_mask, [self.config.num_window, self.config.window_size]) # (num_window, window_size)\n\n mention_doc_flat_embs = self.transform_overlap_sliding_windows_to_original_document(mention_doc_overlap_window_embs, doc_overlap_input_mask) \n mention_doc_flat_embs = tf.reshape(mention_doc_flat_embs, [-1, self.config.hidden_size]) # (num_subtoken_in_doc, hidden_size) \n\n candidate_mention_starts = tf.tile(tf.expand_dims(tf.range(num_subtoken_in_doc), 1), [1, self.config.max_span_width]) # (num_subtoken_in_doc, max_span_width)\n # getting all eligible mentions in each subdocument\n # the number if eligible mentions of each subdocument is config.max_span_width * num_subtoken_in_doc\n candidate_mention_ends = tf.math.add(candidate_mention_starts, tf.expand_dims(tf.range(self.config.max_span_width), 0)) # (num_subtoken_in_doc, max_span_width)\n \n candidate_mention_sentence_start_idx = tf.gather(tf.reshape(flat_doc_sentence_map, [-1]), candidate_mention_starts) # (num_subtoken_in_doc, max_span_width)\n candidate_mention_sentence_end_idx = tf.gather(tf.reshape(flat_doc_sentence_map, [-1]), candidate_mention_ends) # (num_subtoken_in_doc, max_span_width)\n \n candidate_mention_mask = tf.logical_and(candidate_mention_ends < num_subtoken_in_doc, tf.equal(candidate_mention_sentence_start_idx, candidate_mention_sentence_end_idx))\n candidate_mention_mask = tf.reshape(candidate_mention_mask, [-1]) \n\n candidate_mention_starts = self.boolean_mask_1d(tf.reshape(candidate_mention_starts, [-1]), candidate_mention_mask, name_scope=\"candidate_mention_starts\", use_tpu=self.use_tpu)\n candidate_mention_ends = self.boolean_mask_1d(tf.reshape(candidate_mention_ends, [-1]), candidate_mention_mask, name_scope=\"candidate_mention_ends\", use_tpu=self.use_tpu)\n # num_candidate_mention_in_doc is smaller than num_subtoken_in_doc\n\n candidate_cluster_idx_labels = self.get_candidate_cluster_labels(candidate_mention_starts, candidate_mention_ends, gold_start_index_labels, gold_end_index_labels, gold_cluster_ids)\n\n candidate_mention_span_embs, candidate_mention_start_embs, candidate_mention_end_embs = self.get_candidate_span_embedding(\n mention_doc_flat_embs, candidate_mention_starts, candidate_mention_ends) \n # candidate_mention_span_embs -> (num_candidate_mention_in_doc, 2 * hidden_size)\n # candidate_mention_start_embs -> (num_candidate_mention_in_doc, hidden_size)\n # candidate_mention_end_embs -> (num_candidate_mention_in_doc, hidden_size)\n\n gold_label_candidate_mention_spans, gold_label_candidate_mention_starts, gold_label_candidate_mention_ends = self.get_candidate_mention_gold_sequence_label(\n candidate_mention_starts, candidate_mention_ends, gold_start_index_labels, gold_end_index_labels, num_subtoken_in_doc)\n # gold_label_candidate_mention_spans -> (num_candidate_mention_in_doc)\n # gold_label_candidate_mention_starts -> (num_candidate_mention_in_doc)\n # gold_label_candidate_mention_ends -> (num_candidate_mention_in_doc)\n\n mention_proposal_loss, candidate_mention_start_prob, candidate_mention_end_prob, candidate_mention_span_prob, candidate_mention_span_scores = self.get_mention_score_and_loss(\n candidate_mention_span_embs, candidate_mention_start_embs, candidate_mention_end_embs, gold_label_candidate_mention_spans=gold_label_candidate_mention_spans, \n gold_label_candidate_mention_starts=gold_label_candidate_mention_starts, gold_label_candidate_mention_ends=gold_label_candidate_mention_ends, expect_length_of_labels=num_subtoken_in_doc)\n # mention_proposal_loss -> a scalar \n # candidate_mention_start_prob, candidate_mention_end_prob, candidate_mention_span_prob, -> (num_candidate_mention_in_doc)\n\n self.k = tf.minimum(self.config.max_candidate_mentions, tf.to_int32(tf.floor(tf.to_float(num_subtoken_in_doc) * self.config.top_span_ratio)))\n # self.k is a hyper-parameter. We want to select the top self.k mentions from the config.max_span_width * num_subtoken_in_doc mentions.\n\n candidate_mention_span_scores = tf.reshape(candidate_mention_span_scores, [-1])\n topk_mention_span_scores, topk_mention_span_indices = tf.nn.top_k(candidate_mention_span_scores, self.k, sorted=False) \n topk_mention_span_indices = tf.reshape(topk_mention_span_indices, [-1])\n # topk_mention_span_scores -> (k,)\n # topk_mention_span_indices -> (k,)\n\n topk_mention_start_indices = tf.gather(candidate_mention_starts, topk_mention_span_indices) # (k,)\n topk_mention_end_indices = tf.gather(candidate_mention_ends, topk_mention_span_indices) # (k,)\n topk_mention_span_cluster_ids = tf.gather(candidate_cluster_idx_labels, topk_mention_span_indices) # (k,)\n topk_mention_span_scores = tf.gather(candidate_mention_span_scores, topk_mention_span_indices) # (k,)\n ## mention proposal stage ends\n ###########\n ###########\n\n\n ###### mention linking stage starts\n ## foward QA score computation starts\n ## for a given proposed mention i, we first compute the score of a span j being the correferent answer to i, denoted by s(j|i) \n i0 = tf.constant(0)\n forward_qa_input_ids = tf.zeros((1, self.config.num_window, self.config.window_size + self.config.max_query_len + 2), dtype=tf.int32) # (1, num_window, max_query_len + window_size + 2)\n forward_qa_input_mask = tf.zeros((1, self.config.num_window, self.config.window_size + self.config.max_query_len + 2), dtype=tf.int32) # (1, num_window, max_query_len + window_size + 2)\n forward_qa_input_token_type_mask = tf.zeros((1, self.config.num_window, self.config.window_size + self.config.max_query_len + 2), dtype=tf.int32) # (1, num_window, max_query_len + window_size + 2)\n\n # prepare for non-overlap input token ids \n doc_overlap_input_mask = tf.reshape(flat_doc_overlap_input_mask, [self.config.num_window, self.config.window_size])\n nonoverlap_doc_input_ids = self.transform_overlap_sliding_windows_to_original_document(flat_window_input_ids, doc_overlap_input_mask) # (num_subtoken_in_doc)\n overlap_window_input_ids = tf.reshape(flat_window_input_ids, [self.config.num_window, self.config.window_size]) # (num_window, window_size)\n\n @tf.function\n def forward_qa_mention_linking(i, batch_qa_input_ids, batch_qa_input_mask, batch_qa_input_token_type_mask):\n tmp_mention_start_idx = tf.gather(topk_mention_start_indices, i)\n tmp_mention_end_idx = tf.gather(topk_mention_end_indices, i)\n\n query_input_token_ids, mention_start_idx_in_sent, mention_end_idx_in_sent = self.get_query_token_ids(\n nonoverlap_doc_input_ids, flat_doc_sentence_map, tmp_mention_start_idx, tmp_mention_end_idx)\n\n query_pad_token_ids = tf.zeros([self.config.max_query_len - self.get_shape(query_input_token_ids, 0)], dtype=tf.int32)\n\n pad_query_input_token_ids = tf.concat([query_input_token_ids, query_pad_token_ids], axis=0) # (max_query_len,)\n pad_query_input_token_mask = tf.ones_like(pad_query_input_token_ids, tf.int32) # (max_query_len)\n pad_query_input_token_type_mask = tf.zeros_like(pad_query_input_token_ids, tf.int32) # (max_query_len)\n\n\n expand_pad_query_input_token_ids = tf.tile(tf.expand_dims(pad_query_input_token_ids, 0), [self.config.num_window, 1]) # (num_window, max_query_len)\n expand_pad_query_input_token_mask = tf.tile(tf.expand_dims(pad_query_input_token_mask, 0), [self.config.num_window, 1]) # (num_window, max_query_len)\n expand_pad_query_input_token_type_mask = tf.tile(tf.expand_dims(pad_query_input_token_type_mask, 0), [self.config.num_window, 1]) # (num_window, max_query_len)\n\n sep_tokens = tf.cast(tf.fill([self.config.num_window, 1], self.sep_in_vocab), tf.int32) # (num_window, 1)\n cls_tokens = tf.cast(tf.fill([self.config.num_window, 1], self.cls_in_vocab), tf.int32) # (num_window, 1)\n\n query_context_input_token_ids = tf.concat([cls_tokens, expand_pad_query_input_token_ids, sep_tokens, overlap_window_input_ids], axis=1) # (1, num_window, max_query_len + window_size + 2)\n query_context_input_token_mask = tf.concat([tf.ones_like(cls_tokens, tf.int32), expand_pad_query_input_token_mask, tf.ones_like(sep_tokens, tf.int32), tf.ones_like(overlap_window_input_ids, tf.int32)], axis=1) # (1, num_window, max_query_len + window_size + 2)\n query_context_input_token_type_mask = tf.concat([tf.zeros_like(cls_tokens, tf.int32), expand_pad_query_input_token_type_mask, tf.zeros_like(sep_tokens, tf.int32), tf.ones_like(overlap_window_input_ids, tf.int32)], axis=1) # (1, num_window, max_query_len + window_size + 2)\n\n query_context_input_token_ids = tf.reshape(query_context_input_token_ids, [1, self.config.num_window, self.config.max_query_len+self.config.window_size+2])\n query_context_input_token_mask = tf.reshape(query_context_input_token_mask, [1, self.config.num_window, self.config.max_query_len+self.config.window_size+2])\n query_context_input_token_type_mask = tf.reshape(query_context_input_token_type_mask, [1, self.config.num_window, self.config.max_query_len+self.config.window_size+2])\n\n\n return [tf.math.add(i, 1), tf.concat([batch_qa_input_ids, query_context_input_token_ids], 0), \n tf.concat([batch_qa_input_mask, query_context_input_token_mask], 0), \n tf.concat([batch_qa_input_token_type_mask, query_context_input_token_type_mask], 0)]\n\n\n\n _, stack_forward_qa_input_ids, stack_forward_qa_input_mask, stack_forward_qa_input_type_mask = tf.while_loop(\n cond=lambda i, o1, o2, o3 : i < self.k,\n body=forward_qa_mention_linking, \n loop_vars=[i0, forward_qa_input_ids, forward_qa_input_mask, forward_qa_input_token_type_mask], \n shape_invariants=[i0.get_shape(), tf.TensorShape([None, None, None]), \n tf.TensorShape([None, None, None]), tf.TensorShape([None, None, None])])\n\n # stack_forward_qa_input_ids, stack_forward_qa_input_mask, stack_forward_qa_input_type_mask -> (k, num_window, max_query_len + window_size + 2)\n\n batch_forward_qa_input_ids = tf.reshape(stack_forward_qa_input_ids, [-1, self.config.max_query_len+self.config.window_size+2]) # (k * num_window, max_query_len + window_size + 2)\n batch_forward_qa_input_mask = tf.reshape(stack_forward_qa_input_mask, [-1, self.config.max_query_len+self.config.window_size+2]) # (k * num_window, max_query_len + window_size + 2)\n batch_forward_qa_input_type_mask = tf.reshape(stack_forward_qa_input_type_mask, [-1, self.config.max_query_len+self.config.window_size+2]) # (k * num_window, max_query_len + window_size + 2)\n\n forward_qa_linking_model = modeling.BertModel(config=self.bert_config, is_training=is_training, \n input_ids=batch_forward_qa_input_ids, input_mask=batch_forward_qa_input_mask, \n token_type_ids=batch_forward_qa_input_type_mask, use_one_hot_embeddings=False, \n scope=\"bert\")\n\n forward_qa_overlap_window_embs = forward_qa_linking_model.get_sequence_output() # (k * num_window, max_query_len + window_size + 2, hidden_size)\n forward_context_overlap_window_embs = self.transform_overlap_sliding_windows_to_original_document(forward_qa_overlap_window_embs, batch_forward_qa_input_type_mask)\n forward_context_overlap_window_embs = tf.reshape(forward_context_overlap_window_embs, [self.k*self.config.num_window, self.config.window_size])\n # forward_context_overlap_window_embs -> (k*num_window, window_size, hidden_size)\n\n expand_doc_overlap_input_mask = tf.tile(tf.expand_dims(doc_overlap_input_mask, 0), [self.k, 1, 1]) # (k, num_window, window_size)\n expand_doc_overlap_input_mask = tf.reshape(expand_doc_overlap_input_mask, [-1, self.config.window_size]) # (k * num_window, window_size)\n\n forward_context_flat_doc_embs = self.transform_overlap_sliding_windows_to_original_document(forward_context_overlap_window_embs, expand_doc_overlap_input_mask) # (k * num_subtoken_in_doc, hidden_size)\n forward_context_flat_doc_embs = tf.reshape(forward_context_flat_doc_embs, [self.k, -1, self.config.hidden_size]) # (k, num_subtoken_in_doc, hidden_size)\n num_candidate_mention = self.get_shape(candidate_mention_span_embs, 0) # (num_candidate_mention_in_doc)\n forward_qa_mention_pos_offset = tf.cast(tf.tile(tf.reshape(tf.range(0, num_candidate_mention) * num_subtoken_in_doc, [1, -1]), [self.k, 1]), tf.int32) # (k, num_candidate_mention_in_doc)\n\n forward_qa_mention_starts = tf.tile(tf.expand_dims(candidate_mention_starts, 0), [self.k, 1]) + forward_qa_mention_pos_offset # (k, num_candidate_mention_in_doc)\n forward_qa_mention_ends = tf.tile(tf.expand_dims(candidate_mention_ends, 0), [self.k, 1]) + forward_qa_mention_pos_offset # (k, num_candidate_mention_in_doc)\n\n forward_qa_mention_span_embs, forward_qa_mention_start_embs, forward_qa_mention_end_embs = self.get_candidate_span_embedding(tf.reshape(forward_context_flat_doc_embs, \n [-1, self.config.hidden_size]), tf.reshape(forward_qa_mention_starts, [-1]), tf.reshape(forward_qa_mention_ends, [-1]))\n # forward_qa_mention_span_embs -> (k * num_candidate_mention_in_doc, hidden_size*2)\n # forward_qa_mention_start_embs -> (k * num_candidate_mention_in_doc, hidden_size)\n\n self.c = tf.to_int32(tf.minimum(self.config.max_top_antecedents, self.k))\n\n forward_qa_mention_span_scores, forward_qa_mention_start_scores, forward_qa_mention_end_scores = self.get_mention_score_and_loss(forward_qa_mention_span_embs, \n forward_qa_mention_start_embs, forward_qa_mention_end_embs, name_scope=\"forward_qa\") \n # forward_qa_mention_span_prob, forward_qa_mention_start_prob, forward_qa_mention_end_prob -> (k * num_candidate_mention_in_doc)\n\n # computes the s(j|i) for all eligible span j in the document \n if self.config.sec_qa_mention_score:\n forward_qa_mention_span_scores = (forward_qa_mention_span_scores + forward_qa_mention_start_scores + forward_qa_mention_end_scores)/3.0\n else:\n forward_qa_mention_span_scores = forward_qa_mention_span_scores\n\n forward_candidate_mention_span_scores = tf.reshape(forward_qa_mention_span_scores, [self.k, -1]) # (k, num_candidate_mention_in_doc)\n forward_topc_mention_span_scores, local_forward_topc_mention_span_indices = tf.nn.top_k(forward_candidate_mention_span_scores, self.c, sorted=False) # (k, c)\n # for each i, we only maintain the top self.c spans based on s(j|i)\n local_flat_forward_topc_mention_span_indices = tf.reshape(local_forward_topc_mention_span_indices, [-1]) # (k * c)\n\n # topk_mention_start_indices\n forward_topc_mention_start_indices = tf.gather(candidate_mention_starts, local_flat_forward_topc_mention_span_indices) # (k, c)\n forward_topc_mention_end_indices = tf.gather(candidate_mention_ends, local_flat_forward_topc_mention_span_indices) # (k, c)\n forward_topc_mention_span_scores_in_mention_proposal = tf.gather(candidate_mention_span_scores, local_flat_forward_topc_mention_span_indices) # (k, c)\n forward_topc_span_cluster_ids = tf.gather(candidate_cluster_idx_labels, local_flat_forward_topc_mention_span_indices)\n ## foward QA score computation ends\n\n\n ## backward QA score computation begins\n ## we need to compute the score of backward score, i.e., the span i is the correferent answer for j, denoted by s(i|j)\n i0 = tf.constant(0)\n backward_qa_input_ids = tf.reshape(tf.zeros((1, self.config.max_query_len + self.config.max_context_len + 2), dtype=tf.int32), [1, -1]) # (1, max_query_len + max_context_len + 2)\n backward_qa_input_mask = tf.reshape(tf.zeros((1, self.config.max_query_len + self.config.max_context_len + 2), dtype=tf.int32), [1, -1]) # (1, max_query_len + max_context_len + 2)\n backward_qa_input_token_type_mask = tf.reshape(tf.zeros((1, self.config.max_query_len + self.config.max_context_len + 2), dtype=tf.int32), [1, -1]) # (1, max_query_len + max_context_len + 2)\n backward_qa_mention_start_in_context = tf.convert_to_tensor(tf.constant([0]), dtype=tf.int32)\n backward_qa_mention_end_in_context = tf.convert_to_tensor(tf.constant([0]), dtype=tf.int32)\n \n\n @tf.function\n def backward_qa_mention_linking(i, batch_qa_input_ids, batch_qa_input_mask, batch_qa_input_token_type_mask, \n batch_qa_mention_start_in_context, batch_qa_mention_end_in_context):\n\n tmp_query_mention_start_idx = tf.gather(forward_topc_mention_start_indices, i)\n tmp_query_mention_end_idx = tf.gather(forward_topc_mention_end_indices, i)\n\n tmp_index_for_topk_mention = tf.floor_div(i, self.k)\n tmp_context_mention_start_idx = tf.gather(topk_mention_start_indices, tmp_index_for_topk_mention)\n tmp_context_mention_end_idx = tf.gather(topk_mention_end_indices, tmp_index_for_topk_mention)\n\n query_input_token_ids, mention_start_idx_in_query, mention_end_idx_in_query = self.get_query_token_ids(\n nonoverlap_doc_input_ids, flat_doc_sentence_map, tmp_query_mention_start_idx, tmp_query_mention_end_idx)\n\n context_input_token_ids, mention_start_idx_in_context, mention_end_idx_in_context = self.get_query_token_ids(\n nonoverlap_doc_input_ids, flat_doc_sentence_map, tmp_context_mention_start_idx, tmp_context_mention_end_idx)\n\n query_pad_token_ids = tf.zeros([self.config.max_query_len - self.get_shape(query_input_token_ids, 0)], dtype=tf.int32)\n context_pad_token_ids = tf.zeros([self.config.max_context_len - self.get_shape(context_input_token_ids, 0)], dtype=tf.int32)\n\n pad_query_input_token_ids = tf.concat([query_input_token_ids, query_pad_token_ids], axis=0) # (max_query_len)\n pad_query_input_token_mask = tf.ones_like(pad_query_input_token_ids, tf.int32)\n pad_query_input_token_type_mask = tf.zeros_like(pad_query_input_token_ids, tf.int32)\n\n pad_context_input_token_ids = tf.concat([context_input_token_ids, context_pad_token_ids], axis=0) # (max_context_len)\n pad_context_input_token_mask = tf.ones_like(pad_context_input_token_ids, tf.int32)\n pad_context_input_token_type_mask = tf.ones_like(pad_context_input_token_ids, tf.int32)\n\n sep_tokens = tf.cast(tf.fill([1], self.sep_in_vocab), tf.int32) # (num_window, 1)\n cls_tokens = tf.cast(tf.fill([1], self.cls_in_vocab), tf.int32) # (num_window, 1)\n\n query_context_input_token_ids = tf.concat([cls_tokens, pad_query_input_token_ids, sep_tokens, pad_context_input_token_ids], axis=0)\n query_context_input_token_mask = tf.concat([tf.ones_like(cls_tokens, tf.int32), pad_query_input_token_mask, tf.zeros_like(sep_tokens, tf.int32), pad_context_input_token_mask], axis=0)\n query_context_input_token_type_mask = tf.concat([tf.zeros_like(cls_tokens, tf.int32), pad_query_input_token_type_mask, tf.zeros_like(sep_tokens, tf.int32), pad_context_input_token_type_mask], axis=0)\n\n query_context_input_token_ids = tf.reshape(query_context_input_token_ids, [1, self.config.max_query_len + self.config.max_context_len + 2])\n query_context_input_token_mask = tf.reshape(query_context_input_token_mask, [1, self.config.max_query_len + self.config.max_context_len + 2])\n query_context_input_token_type_mask = tf.reshape(query_context_input_token_type_mask, [1, self.config.max_query_len + self.config.max_context_len + 2])\n mention_start_idx_in_context = tf.reshape(mention_start_idx_in_context, [-1])\n mention_end_idx_in_context = tf.reshape(mention_end_idx_in_context, [-1])\n\n return [tf.math.add(i, 1), tf.concat([batch_qa_input_ids, query_context_input_token_ids], 0), \n tf.concat([batch_qa_input_mask, query_context_input_token_mask], 0), \n tf.concat([batch_qa_input_token_type_mask, query_context_input_token_type_mask], 0), \n tf.concat([backward_qa_mention_start_in_context, mention_start_idx_in_context], 0), \n tf.concat([backward_qa_mention_end_in_context, mention_end_idx_in_context], 0)]\n\n\n _, stack_backward_qa_input_ids, stack_backward_qa_input_mask, stack_backward_qa_input_type_mask, stack_backward_mention_start_in_context, stack_backward_mention_end_in_context = tf.while_loop(\n cond = lambda i, o1, o2, o3, o4, o5: i < self.k * self.c,\n body=backward_qa_mention_linking, \n loop_vars=[i0, backward_qa_input_ids, backward_qa_input_mask, backward_qa_input_token_type_mask, backward_qa_mention_start_in_context, backward_qa_mention_end_in_context], \n shape_invariants=[i0.get_shape(), tf.TensorShape([None, None]), tf.TensorShape([None, None]), tf.TensorShape([None, None]), \n tf.TensorShape([None]), tf.TensorShape([None])]) \n\n # stack_backward_qa_input_ids, stack_backward_qa_input_mask, stack_backward_qa_input_type_mask -> (k*c, max_query_len + max_context_len + 2)\n # stack_backward_mention_start_in_context, stack_backward_mention_end_in_context -> (k*c,)\n\n batch_backward_qa_input_ids = tf.reshape(stack_backward_qa_input_ids, [-1, self.config.max_query_len+self.config.max_context_len+2])\n batch_backward_qa_input_mask = tf.reshape(stack_backward_qa_input_mask, [-1, self.config.max_query_len+self.config.max_context_len+2])\n batch_backward_qa_input_type_mask = tf.reshape(stack_backward_qa_input_type_mask, [-1, self.config.max_query_len+self.config.max_context_len+2])\n\n backward_qa_linking_model = modeling.BertModel(config=self.bert_config, is_training=is_training, \n input_ids=batch_backward_qa_input_ids, input_mask=batch_backward_qa_input_mask, \n token_type_ids=batch_backward_qa_input_type_mask, use_one_hot_embeddings=False, \n scope=\"bert\")\n\n backward_query_context_embs = backward_qa_linking_model.get_sequence_output() # (k*c, max_query_len + max_context_len + 2, hidden_size)\n backward_query_context_embs = tf.reshape(backward_query_context_embs, [self.k*self.c, -1, self.config.hidden_size])\n flat_batch_backward_qa_input_type_mask = tf.reshape(batch_backward_qa_input_type_mask, [self.k*self.c, -1])\n\n backward_context_flat_embs = self.transform_overlap_sliding_windows_to_original_document(backward_query_context_embs, flat_batch_backward_qa_input_type_mask) # (k*c, max_context_len, hidden_size)\n batch_backward_mention_start_in_context = tf.reshape(stack_backward_mention_start_in_context, [-1]) + tf.range(0, self.c*self.k) * (self.config.max_query_len+self.config.max_context_len) \n batch_backward_mention_end_in_context = tf.reshape(stack_backward_mention_end_in_context, [-1]) + tf.range(0, self.c*self.k) * (self.config.max_query_len+self.config.max_context_len) \n\n backward_qa_mention_span_embs, backward_qa_mention_start_embs, backward_qa_mention_end_embs = self.get_candidate_span_embedding(tf.reshape(backward_context_flat_embs, \n [-1, self.config.hidden_size]), tf.reshape(batch_backward_mention_start_in_context, [-1]), tf.reshape(batch_backward_mention_end_in_context, [-1]))\n # backward_qa_mention_span_embs -> (k*c, 2*hidden_size)\n # backward_qa_mention_start_embs, backward_qa_mention_end_embs -> (k*c, hidden_size)\n\n backward_qa_mention_span_scores, backward_qa_mention_start_scores, backward_qa_mention_end_scores = self.get_mention_score_and_loss(backward_qa_mention_span_embs, \n backward_qa_mention_start_embs, backward_qa_mention_end_embs, name_scope=\"backward_qa\")\n # backward_qa_mention_span_prob -> (k*c)\n # backward_qa_mention_start_prob, backward_qa_mention_end_prob -> (k*c)\n\n if self.config.sec_qa_mention_score:\n backward_qa_mention_span_scores = (backward_qa_mention_span_scores + backward_qa_mention_start_scores + backward_qa_mention_end_scores)/3.0\n else:\n backward_qa_mention_span_scores = backward_qa_mention_span_scores \n #############\n ############# backward QA computation ends\n \n forward_topc_mention_span_scores = tf.reshape(forward_topc_mention_span_scores, [-1])\n expand_forward_topc_mention_span_scores = tf.tile(tf.expand_dims(forward_topc_mention_span_scores, 0), [self.k, 1]) # forward_topc_mention_span_scores -> (c); expand_forward_topc_mention_span_scores -> (c, k)\n expand_forward_topc_mention_span_scores_in_mention_proposal = tf.tile(tf.expand_dims(forward_topc_mention_span_scores_in_mention_proposal, 0), [self.k, 1])\n expand_topk_mention_span_scores = tf.tile(tf.expand_dims(topk_mention_span_scores, 1), [1, self.c]) # (k, c)\n\n backward_qa_mention_span_scores = tf.reshape(backward_qa_mention_span_scores, [self.k, self.c]) # (k, c)\n\n mention_span_linking_scores = (expand_forward_topc_mention_span_scores + backward_qa_mention_span_scores ) / 2.0 \n mention_span_linking_scores = mention_span_linking_scores+ expand_forward_topc_mention_span_scores_in_mention_proposal + expand_topk_mention_span_scores\n mention_span_linking_scores = tf.reshape(mention_span_linking_scores, [self.k, self.c]) # (k, c)\n dummy_scores = tf.zeros([self.k, 1]) # (k, 1)\n\n top_mention_span_linking_scores = tf.concat([dummy_scores, mention_span_linking_scores], axis=1) # (k, c)\n\n forward_topc_span_cluster_ids = tf.reshape(forward_topc_span_cluster_ids, [self.k, self.c]) # (k, c)\n same_cluster_indicator = tf.equal(forward_topc_span_cluster_ids, tf.expand_dims(topk_mention_span_cluster_ids, 1)) \n non_dummy_indicator = tf.expand_dims(topk_mention_span_cluster_ids > 0, 1)\n pairwise_labels = tf.logical_and(same_cluster_indicator, non_dummy_indicator)\n dummy_labels = tf.logical_not(tf.reduce_any(pairwise_labels, 1, keepdims=True)) \n top_mention_span_linking_labels = tf.concat([dummy_labels, pairwise_labels], 1)\n\n linking_loss = self.marginal_likelihood_loss(top_mention_span_linking_scores, top_mention_span_linking_labels)\n\n total_loss = mention_proposal_loss + linking_loss \n\n return total_loss, (topk_mention_start_indices, topk_mention_end_indices), (forward_topc_mention_start_indices, forward_topc_mention_end_indices), top_mention_span_linking_scores \n\n\n def marginal_likelihood_loss(self, antecedent_scores, antecedent_labels):\n \"\"\"\n Desc:\n marginal likelihood of gold antecedent spans form coreference cluster \n Args:\n antecedent_scores: [k, c+1] the predicted scores by the model\n antecedent_labels: [k, c+1] the gold-truth cluster labels\n Returns:\n a scalar of loss \n \"\"\"\n gold_scores = tf.math.add(antecedent_scores, tf.log(tf.to_float(antecedent_labels)))\n marginalized_gold_scores = tf.math.reduce_logsumexp(gold_scores, [1]) # [k]\n log_norm = tf.math.reduce_logsumexp(antecedent_scores, [1]) # [k]\n loss = log_norm - marginalized_gold_scores # [k]\n return tf.math.reduce_sum(loss)\n\n\n def get_query_token_ids(self, nonoverlap_doc_input_ids, sentence_map, mention_start_idx, mention_end_idx, paddding=True):\n \"\"\"\n Desc:\n construct question based on the selected mention. \n \"\"\"\n nonoverlap_doc_input_ids = tf.reshape(nonoverlap_doc_input_ids, [-1])\n\n sentence_idx_for_mention = tf.gather(sentence_map, mention_start_idx)\n sentence_mask_for_mention = tf.math.equal(sentence_map, sentence_idx_for_mention)\n query_token_input_ids = self.boolean_mask_1d(nonoverlap_doc_input_ids, sentence_mask_for_mention, name_scope=\"query_mention\", use_tpu=self.use_tpu)\n\n sentence_start = tf.where(tf.equal(nonoverlap_doc_input_ids, tf.gather(query_token_input_ids, tf.constant(0))))\n\n mention_start_in_sent = mention_start_idx - tf.cast(sentence_start, tf.int32) \n mention_end_in_sent = mention_end_idx - tf.cast(sentence_start, tf.int32) \n\n return query_token_input_ids, mention_start_in_sent, mention_end_in_sent \n\n\n\n def get_mention_score_and_loss(self, candidate_mention_span_embs, candidate_mention_start_embs, candidate_mention_end_embs, \n gold_label_candidate_mention_spans=None, gold_label_candidate_mention_starts=None, gold_label_candidate_mention_ends=None, expect_length_of_labels=None, \n name_scope=\"mention\"):\n\n candidate_mention_span_logits = self.ffnn(candidate_mention_span_embs, self.config.hidden_size*2, 1, dropout=self.dropout, name_scope=\"{}_span\".format(name_scope))\n candidate_mention_start_logits = self.ffnn(candidate_mention_start_embs, self.config.hidden_size, 1, dropout=self.dropout, name_scope=\"{}_start\".format(name_scope))\n candidate_mention_end_logits = self.ffnn(candidate_mention_end_embs, self.config.hidden_size, 1, dropout=self.dropout, name_scope=\"{}_end\".format(name_scope))\n\n if gold_label_candidate_mention_spans is None or gold_label_candidate_mention_starts is None or gold_label_candidate_mention_ends is None: \n candidate_mention_span_scores = tf.math.log(tf.sigmoid(candidate_mention_span_logits))\n candidate_mention_start_scores = tf.math.log(tf.sigmoid(candidate_mention_start_logits))\n candidate_mention_end_scores = tf.math.log(tf.sigmoid(candidate_mention_end_logits))\n\n return candidate_mention_span_scores, candidate_mention_start_scores, candidate_mention_end_scores\n\n\n start_loss, candidate_mention_start_probability = self.compute_mention_score_and_loss(candidate_mention_start_logits, gold_label_candidate_mention_starts)\n end_loss, candidate_mention_end_probability = self.compute_mention_score_and_loss(candidate_mention_end_logits, gold_label_candidate_mention_ends)\n span_loss, candidate_mention_span_probability = self.compute_mention_score_and_loss(candidate_mention_span_logits, gold_label_candidate_mention_spans)\n\n \n total_loss = start_loss + end_loss + span_loss\n candidate_mention_span_scores = (tf.math.log(candidate_mention_start_probability) + tf.math.log(candidate_mention_end_probability) + tf.math.log(candidate_mention_span_probability)) / 3.0 \n\n return total_loss, candidate_mention_start_probability, candidate_mention_end_probability, candidate_mention_span_probability, candidate_mention_span_scores\n\n\n def compute_mention_score_and_loss(self, pred_sequence_logits, gold_sequence_labels, loss_mask=None):\n \"\"\"\n Desc:\n compute the unifrom start/end loss and probabilities. \n Args:\n pred_sequence_logits: (input_shape, 1) \n gold_sequence_labels: (input_shape, 1)\n loss_mask: [optional] if is not None, it should be (input_shape). should be tf.int32 0/1 tensor. \n FOR start/end score and loss, input_shape should be num_subtoken_in_doc.\n FOR span score and loss, input_shape should be num_subtoken_in_doc * num_subtoken_in_doc. \n \"\"\"\n pred_sequence_probabilities = tf.cast(tf.reshape(tf.sigmoid(pred_sequence_logits), [-1]),tf.float32) # (input_shape)\n expand_pred_sequence_scores = tf.stack([(1 - pred_sequence_probabilities), pred_sequence_probabilities], axis=-1) # (input_shape, 2)\n expand_gold_sequence_labels = tf.cast(tf.one_hot(tf.reshape(gold_sequence_labels, [-1]), 2, axis=-1), tf.float32) # (input_shape, 2)\n\n loss = tf.keras.losses.binary_crossentropy(expand_gold_sequence_labels, expand_pred_sequence_scores)\n # loss -> shape is (input_shape)\n\n if loss_mask is not None:\n loss = tf.multiply(loss, tf.cast(loss_mask, tf.float32))\n\n total_loss = tf.reduce_mean(loss)\n # total_loss -> a scalar \n\n return total_loss, pred_sequence_probabilities\n\n\n def get_candidate_span_embedding(self, doc_sequence_embeddings, candidate_span_starts, candidate_span_ends):\n doc_sequence_embeddings = tf.reshape(doc_sequence_embeddings, [-1, self.config.hidden_size])\n\n span_start_embedding = tf.gather(doc_sequence_embeddings, candidate_span_starts)\n span_end_embedding = tf.gather(doc_sequence_embeddings, candidate_span_ends)\n span_embedding = tf.concat([span_start_embedding, span_end_embedding], 1) \n\n return span_embedding, span_start_embedding, span_end_embedding \n\n def get_candidate_mention_gold_sequence_label(self, candidate_mention_starts, candidate_mention_ends, \n gold_start_index_labels, gold_end_index_labels, expect_length_of_labels):\n\n gold_start_sequence_label = self.scatter_gold_index_to_label_sequence(gold_start_index_labels, expect_length_of_labels)\n gold_end_sequence_label = self.scatter_gold_index_to_label_sequence(gold_end_index_labels, expect_length_of_labels)\n\n gold_label_candidate_mention_starts = tf.gather(gold_start_sequence_label, candidate_mention_starts)\n gold_label_candidate_mention_ends = tf.gather(gold_end_sequence_label, candidate_mention_ends)\n\n gold_mention_sparse_label = tf.reshape(tf.stack([gold_start_index_labels, gold_end_index_labels], axis=1), [2, -1])\n gold_span_value = tf.reshape(tf.ones_like(gold_start_index_labels, tf.int32), [-1])\n gold_span_shape = tf.Variable([expect_length_of_labels, expect_length_of_labels], shape=(2, )) \n gold_span_label = tf.cast(tf.scatter_nd(gold_mention_sparse_label, gold_span_value, gold_span_shape), tf.int32)\n\n candidate_mention_spans = tf.stack([candidate_mention_starts, candidate_mention_ends], axis=1)\n gold_label_candidate_mention_spans = tf.gather_nd(gold_span_label, tf.expand_dims(candidate_mention_spans, 1))\n \n return gold_label_candidate_mention_spans, gold_label_candidate_mention_starts, gold_label_candidate_mention_ends\n\n\n def scatter_gold_index_to_label_sequence(self, gold_index_labels, expect_length_of_labels):\n \"\"\"\n Desc:\n transform the mention start/end position index tf.int32 Tensor to a tf.int32 Tensor with 1/0 labels for the input subtoken sequences.\n 1 denotes this subtoken is the start/end for a mention. \n Args:\n gold_index_labels: a tf.int32 Tensor with mention start/end position index in the original document. \n expect_length_of_labels: the number of subtokens in the original document. \n \"\"\"\n gold_labels_pos = tf.reshape(gold_index_labels, [-1, 1]) # (num_of_mention, 1)\n gold_value = tf.reshape(tf.ones_like(gold_index_labels), [-1]) # (num_of_mention)\n label_shape = tf.Variable(expect_length_of_labels) \n label_shape = tf.reshape(label_shape, [1]) # [1]\n gold_label_sequence = tf.cast(tf.scatter_nd(gold_labels_pos, gold_value, label_shape), tf.int32) # (num_subtoken_in_doc)\n return gold_label_sequence\n\n\n def scatter_span_sequence_labels(self, gold_start_index_labels, gold_end_index_labels, expect_length_of_labels):\n \"\"\"\n Desc:\n transform the mention (start, end) position pairs to a span matrix gold_span_sequence_labels. \n matrix[i][j]: whether the subtokens between the position $i$ to $j$ can be a mention. \n if matrix[i][j] == 0: from $i$ to $j$ is not a mention. \n if matrix[i][j] == 1: from $i$ to $j$ is a mention.\n Args:\n gold_start_index_labels: a tf.int32 Tensor with mention start position index in the original document. \n gold_end_index_labels: a tf.int32 Tensor with mention end position index in the original document. \n expect_length_of_labels: a scalar, should be the same with num_subtoken_in_doc\n \"\"\" \n gold_span_index_labels = tf.stack([gold_start_index_labels, gold_end_index_labels], axis=1) # (num_of_mention, 2)\n gold_span_value = tf.reshape(tf.ones_like(gold_start_index_labels, tf.int32), [-1]) # (num_of_mention)\n gold_span_label_shape = tf.Variable([expect_length_of_labels, expect_length_of_labels]) \n gold_span_label_shape = tf.reshape(gold_span_label_shape, [-1])\n\n gold_span_sequence_labels = tf.cast(tf.scatter_nd(gold_span_index_labels, gold_span_value, gold_span_label_shape), tf.int32) # (num_subtoken_in_doc, num_subtoken_in_doc)\n return gold_span_sequence_labels \n\n\n def get_candidate_cluster_labels(self, candidate_mention_starts, candidate_mention_ends, \n gold_mention_starts, gold_mention_ends, gold_cluster_ids):\n \n same_mention_start = tf.equal(gold_mention_starts, candidate_mention_starts)\n same_mention_end = tf.equal(gold_mention_ends, candidate_mention_ends) \n # same_mention_start = tf.equal(tf.expand_dims(gold_mention_starts, 1), tf.expand_dims(candidate_mention_starts, 0))\n # same_mention_end = tf.equal(tf.expand_dims(gold_mention_ends, 1), tf.expand_dims(candidate_mention_ends, 0)) \n same_mention_span = tf.logical_and(same_mention_start, same_mention_end)\n \n candidate_cluster_idx_labels = tf.matmul(tf.expand_dims(gold_cluster_ids, 0), tf.to_int32(same_mention_span)) # [1, num_candidates]\n candidate_cluster_idx_labels = tf.squeeze(candidate_cluster_idx_labels, 0) # [num_candidates]\n\n return candidate_cluster_idx_labels \n\n\n def transform_overlap_sliding_windows_to_original_document(self, overlap_window_inputs, overlap_window_mask):\n \"\"\"\n Desc:\n hidden_size should be equal to embeddding_size. \n Args:\n doc_overlap_window_embs: (num_window, window_size, hidden_size). \n the output of (num_window, window_size) input_ids forward into BERT model. \n doc_overlap_input_mask: (num_window, window_size). A tf.int32 Tensor contains 0/1. \n 0 represents token in this position should be neglected. 1 represents token in this position should be reserved. \n \"\"\"\n ones_input_mask = tf.ones_like(overlap_window_mask, tf.int32) # (num_window, window_size)\n cumsum_input_mask = tf.math.cumsum(ones_input_mask, axis=1) # (num_window, window_size) # do not equal to cumsum_input_mask -> (num_window, window_size)\n # offset_input_mask = tf.tile(tf.expand_dims(tf.range(self.config.num_window) * self.config.window_size, 1), [1, self.config.window_size]) # (num_window, window_size)\n row_cumsum_input_mask = self.get_shape(cumsum_input_mask, 0)\n col_cumsum_input_mask = self.get_shape(cumsum_input_mask, 1)\n offset_input_mask = tf.tile(tf.expand_dims(tf.range(row_cumsum_input_mask) * col_cumsum_input_mask, 1), [1, col_cumsum_input_mask])\n\n\n offset_cumsum_input_mask = offset_input_mask + cumsum_input_mask # (num_window, window_size)\n global_input_mask = tf.math.multiply(ones_input_mask, offset_cumsum_input_mask) # (num_window, window_size)\n global_input_mask = tf.reshape(global_input_mask, [-1]) # (num_window * window_size)\n global_input_mask_index = self.boolean_mask_1d(global_input_mask, tf.math.greater(global_input_mask, tf.zeros_like(global_input_mask, tf.int32))) # (num_subtoken_in_doc)\n\n overlap_window_inputs = tf.reshape(overlap_window_inputs, [self.config.num_window * self.config.window_size, -1]) # (num_window * window_size, hidden_size)\n original_doc_inputs = tf.gather(overlap_window_inputs, global_input_mask_index) # (num_subtoken_in_doc, hidden_size)\n\n return original_doc_inputs\n\n\n def ffnn(self, inputs, hidden_size, output_size, dropout=None, name_scope=\"fully-conntected-neural-network\",\n hidden_initializer=tf.truncated_normal_initializer(stddev=0.02)):\n \"\"\"\n Desc:\n fully-connected neural network. \n transform non-linearly the [input] tensor with [hidden_size] to a fix [output_size] size. \n Args: \n hidden_size: should be the size of last dimension of [inputs]. \n \"\"\"\n with tf.variable_scope(name_scope, reuse=tf.AUTO_REUSE):\n hidden_weights = tf.get_variable(\"hidden_weights\", [hidden_size, output_size],\n initializer=hidden_initializer)\n hidden_bias = tf.get_variable(\"hidden_bias\", [output_size], initializer=tf.zeros_initializer())\n outputs = tf.nn.relu(tf.nn.xw_plus_b(inputs, hidden_weights, hidden_bias))\n\n if dropout is not None:\n outputs = tf.nn.dropout(outputs, dropout)\n return outputs\n\n\n def boolean_mask_1d(self, itemtensor, boolmask_indicator, name_scope=\"boolean_mask1d\", use_tpu=False):\n \"\"\"\n Desc:\n the same functionality of tf.boolean_mask. \n The tf.boolean_mask operation is not available on the cloud TPU. \n Args:\n itemtensor : a Tensor contains [tf.int32, tf.float32] numbers. Should be 1-Rank.\n boolmask_indicator : a tf.bool Tensor. Should be 1-Rank. \n scope : name scope for the operation. \n use_tpu : if False, return tf.boolean_mask. \n \"\"\"\n with tf.name_scope(name_scope):\n\n boolmask_sum = tf.reduce_sum(tf.cast(boolmask_indicator, tf.int32))\n selected_positions = tf.cast(boolmask_indicator, dtype=tf.float32)\n indexed_positions = tf.cast(tf.multiply(tf.cumsum(selected_positions), selected_positions),dtype=tf.int32)\n one_hot_selector = tf.one_hot(indexed_positions - 1, boolmask_sum, dtype=tf.float32)\n sampled_indices = tf.cast(tf.tensordot(tf.cast(tf.range(tf.shape(boolmask_indicator)[0]), dtype=tf.float32),\n one_hot_selector,axes=[0, 0]),dtype=tf.int32)\n sampled_indices = tf.reshape(sampled_indices, [-1])\n mask_itemtensor = tf.gather(itemtensor, sampled_indices)\n\n return mask_itemtensor\n\n\n def get_dropout(self, dropout_rate, is_training):\n return 1 - (tf.to_float(is_training) * dropout_rate)\n\n\n def get_shape(self, x, dim):\n \"\"\"\n Desc:\n return the size of input x in DIM. \n \"\"\" \n return x.get_shape()[dim].value or tf.shape(x)[dim]\n\n\n def evaluate(self, top_span_starts, top_span_ends, predicted_antecedents, \n gold_clusters, gold_starts, gold_ends):\n \"\"\"\n Desc:\n expected cluster ids is : [[[21, 25], [18, 18]], [[63, 65], [46, 48], [27, 29]], [[88, 88], [89, 89]]]\n Args:\n top_span_starts: \n top_span_ends:\n predicted_antecedents: \n Returns:\n predicted_clusters: \n gold_clusters:\n mention_to_predicted:\n mention_to_gold: \n \"\"\" \n # predicted_antecedents = np.argmax(predicted_antecedents, axis=-1)\n top_span_starts, top_span_ends, predicted_antecedents = top_span_starts.tolist(), top_span_ends.tolist(), predicted_antecedents.tolist()\n gold_clusters, gold_starts, gold_ends = gold_clusters.tolist()[0], gold_starts.tolist()[0], gold_ends.tolist()[0]\n\n def transform_gold_labels(gold_clusters, gold_starts, gold_ends):\n gold_clusters_idx = [tmp for tmp in gold_clusters if tmp >= 0]\n gold_starts = [tmp for tmp in gold_starts if tmp >= 0]\n gold_ends = [tmp for tmp in gold_ends if tmp >= 0]\n\n gold_clusters_dict = {}\n gold_cluster_lst = []\n\n for idx, (tmp_start, tmp_end) in enumerate(zip(gold_starts, gold_ends)):\n tmp_cluster_idx = gold_clusters_idx[idx]\n if tmp_cluster_idx not in gold_clusters_dict.keys():\n gold_cluster_lst.append(tmp_cluster_idx)\n gold_clusters_dict[tmp_cluster_idx] = [[tmp_start, tmp_end]]\n else:\n gold_clusters_dict[tmp_cluster_idx].append([tmp_start, tmp_end])\n\n gold_cluster = [gold_clusters_dict[tmp_idx] for tmp_idx in gold_cluster_lst]\n\n return gold_cluster, gold_starts, gold_ends\n\n gold_clusters, gold_starts, gold_ends = transform_gold_labels(gold_clusters, gold_starts, gold_ends)\n\n gold_clusters = [tuple(tuple(m) for m in gc) for gc in gold_clusters]\n mention_to_gold = {}\n for gc in gold_clusters:\n for mention in gc:\n mention_to_gold[mention] = gc\n\n predicted_clusters, mention_to_predicted = self.get_predicted_clusters(top_span_starts, top_span_ends, predicted_antecedents)\n \n return predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold\n\n\n"
},
{
"path": "models/mention_proposal.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# the mention proposal model for pre-training the Span-BERT model. \n\n\nimport os\nimport sys \n\nrepo_path = \"/\".join(os.path.realpath(__file__).split(\"/\")[:-2])\nif repo_path not in sys.path:\n sys.path.insert(0, repo_path)\n\nimport tensorflow as tf\nfrom bert import modeling\n\n\n\nclass MentionProposalModel(object):\n def __init__(self, config):\n self.config = config \n self.bert_config = modeling.BertConfig.from_json_file(config.bert_config_file)\n self.bert_config.hidden_dropout_prob = config.dropout_rate\n\n def get_mention_proposal_and_loss(self, instance, is_training, use_tpu=False):\n \"\"\"\n Desc:\n forward function for training mention proposal module. \n Args:\n instance: a tuple of train/dev/test data instance. \n e.g., (flat_input_ids, flat_doc_overlap_input_mask, flat_sentence_map, text_len, speaker_ids, gold_starts, gold_ends, cluster_ids)\n is_training: True/False is in the training process. \n \"\"\"\n self.use_tpu = use_tpu \n self.dropout = self.get_dropout(self.config.dropout_rate, is_training)\n\n flat_input_ids, flat_doc_overlap_input_mask, flat_sentence_map, text_len, speaker_ids, gold_starts, gold_ends, cluster_ids = instance\n # flat_input_ids: (num_window, window_size)\n # flat_doc_overlap_input_mask: (num_window, window_size)\n # flat_sentence_map: (num_window, window_size)\n # text_len: dynamic length and is padded to fix length\n # gold_start: (max_num_mention), mention start index in the original (NON-OVERLAP) document. Pad with -1 to the fix length max_num_mention.\n # gold_end: (max_num_mention), mention end index in the original (NON-OVERLAP) document. Pad with -1 to the fix length max_num_mention.\n # cluster_ids/speaker_ids is not used in the mention proposal model.\n\n flat_input_ids = tf.math.maximum(flat_input_ids, tf.zeros_like(flat_input_ids, tf.int32)) # (num_window * window_size)\n \n flat_doc_overlap_input_mask = tf.where(tf.math.greater_equal(flat_doc_overlap_input_mask, 0), \n x=tf.ones_like(flat_doc_overlap_input_mask, tf.int32), y=tf.zeros_like(flat_doc_overlap_input_mask, tf.int32)) # (num_window * window_size)\n # flat_doc_overlap_input_mask = tf.math.maximum(flat_doc_overlap_input_mask, tf.zeros_like(flat_doc_overlap_input_mask, tf.int32))\n flat_sentence_map = tf.math.maximum(flat_sentence_map, tf.zeros_like(flat_sentence_map, tf.int32)) # (num_window * window_size)\n \n gold_start_end_mask = tf.cast(tf.math.greater_equal(gold_starts, tf.zeros_like(gold_starts, tf.int32)), tf.bool) # (max_num_mention)\n gold_start_index_labels = self.boolean_mask_1d(gold_starts, gold_start_end_mask, name_scope=\"gold_starts\", use_tpu=self.use_tpu) # (num_of_mention)\n gold_end_index_labels = self.boolean_mask_1d(gold_ends, gold_start_end_mask, name_scope=\"gold_ends\", use_tpu=self.use_tpu) # (num_of_mention)\n\n text_len = tf.math.maximum(text_len, tf.zeros_like(text_len, tf.int32)) # (num_of_non_empty_window)\n num_subtoken_in_doc = tf.math.reduce_sum(text_len) # the value should be num_subtoken_in_doc \n\n input_ids = tf.reshape(flat_input_ids, [-1, self.config.window_size]) # (num_window, window_size)\n input_mask = tf.ones_like(input_ids, tf.int32) # (num_window, window_size)\n\n model = modeling.BertModel(config=self.bert_config, is_training=is_training, \n input_ids=input_ids, input_mask=input_mask, \n use_one_hot_embeddings=False, scope='bert')\n\n doc_overlap_window_embs = model.get_sequence_output() # (num_window, window_size, hidden_size)\n doc_overlap_input_mask = tf.reshape(flat_doc_overlap_input_mask, [self.config.num_window, self.config.window_size]) # (num_window, window_size)\n\n doc_flat_embs = self.transform_overlap_windows_to_original_doc(doc_overlap_window_embs, doc_overlap_input_mask) \n doc_flat_embs = tf.reshape(doc_flat_embs, [-1, self.config.hidden_size]) # (num_subtoken_in_doc, hidden_size)\n\n expand_start_embs = tf.tile(tf.expand_dims(doc_flat_embs, 1), [1, num_subtoken_in_doc, 1]) # (num_subtoken_in_doc, num_subtoken_in_doc, hidden_size)\n expand_end_embs = tf.tile(tf.expand_dims(doc_flat_embs, 0), [num_subtoken_in_doc, 1, 1]) # (num_subtoken_in_doc, num_subtoken_in_doc, hidden_size)\n expand_mention_span_embs = tf.concat([expand_start_embs, expand_end_embs], axis=-1) # (num_subtoken_in_doc, num_subtoken_in_doc, 2*hidden_size)\n expand_mention_span_embs = tf.reshape(expand_mention_span_embs, [-1, self.config.hidden_size*2])\n span_sequence_logits = self.ffnn(expand_mention_span_embs, self.config.hidden_size*2, 1, dropout=self.dropout, name_scope=\"mention_span\") # (num_subtoken_in_doc * num_subtoken_in_doc)\n\n if self.config.start_end_share:\n start_end_sequence_logits = self.ffnn(doc_flat_embs, self.config.hidden_size, 2, dropout=self.dropout, name_scope=\"mention_start_end\") # (num_subtoken_in_doc, 2)\n start_sequence_logits, end_sequence_logits = tf.split(start_end_sequence_logits, axis=1)\n # start_sequence_logits -> (num_subtoken_in_doc, 1)\n # end_sequence_logits -> (num_subtoken_in_doc, 1)\n else:\n start_sequence_logits = self.ffnn(doc_flat_embs, self.config.hidden_size, 1, dropout=self.dropout, name_scope=\"mention_start\") # (num_subtoken_in_doc)\n end_sequence_logits = self.ffnn(doc_flat_embs, self.config.hidden_size, 1, dropout=self.dropout, name_scope=\"mention_end\") # (num_subtoken_in_doc)\n\n gold_start_sequence_labels = self.scatter_gold_index_to_label_sequence(gold_start_index_labels, num_subtoken_in_doc) # (num_subtoken_in_doc)\n gold_end_sequence_labels = self.scatter_gold_index_to_label_sequence(gold_end_index_labels, num_subtoken_in_doc) # (num_subtoken_in_doc)\n\n start_loss, start_sequence_probabilities = self.compute_score_and_loss(start_sequence_logits, gold_start_sequence_labels)\n end_loss, end_sequence_probabilities = self.compute_score_and_loss(end_sequence_logits, gold_end_sequence_labels)\n # *_loss -> a scalar \n # *_sequence_scores -> (num_subtoken_in_doc)\n\n gold_span_sequence_labels = self.scatter_span_sequence_labels(gold_start_index_labels, gold_end_index_labels, num_subtoken_in_doc) # (num_subtoken_in_doc * num_subtoken_in_doc)\n span_loss, span_sequence_probabilities = self.compute_score_and_loss(span_sequence_logits, gold_span_sequence_labels)\n # span_loss -> a scalar \n # span_sequence_probabilities -> (num_subtoken_in_doc * num_subtoken_in_doc)\n \n total_loss = self.config.loss_start_ratio * start_loss + self.config.loss_end_ratio * end_loss + self.config.loss_span_ratio * span_loss \n return total_loss, start_sequence_probabilities, end_sequence_probabilities, span_sequence_probabilities\n\n\n def get_gold_mention_sequence_labels_from_pad_index(self, pad_gold_start_index_labels, pad_gold_end_index_labels, pad_text_len):\n \"\"\"\n Desc:\n the original gold labels is padded to the fixed length and only contains the position index of gold mentions. \n return the gold sequence of labels for evaluation. \n Args:\n pad_gold_start_index_labels: a tf.int32 tensor with a fixed length (self.config.max_num_mention). \n every element in the tensor is the start position index for the mentions. \n pad_gold_end_index_labels: a tf.int32 tensor with a fixed length (self.config.max_num_mention). \n every element in the tensor is the end position index of the mentions. \n pad_text_len: a tf.int32 tensor with a fixed length (self.config.num_window). \n every positive element in the tensor indicates that the number of subtokens in the window. \n Returns:\n gold_start_sequence_labels: a tf.int32 tensor with the shape of (num_subtoken_in_doc). \n if the element in the tensor equals to 0, this subtoken is not a start for a mention. \n if the elemtn in the tensor equals to 1, this subtoken is a start for a mention. \n gold_end_sequence_labels: a tf.int32 tensor with the shape of (num_subtoken_in_doc). \n if the element in the tensor equals to 0, this subtoken is not a end for a mention. \n if the elemtn in the tensor equals to 1, this subtoken is a end for a mention. \n gold_span_sequence_labels: a tf.int32 tensor with the shape of (num_subtoken_in_doc, num_subtoken_in_doc)/ \n if the element[i][j] equals to 0, this subtokens from $i$ to $j$ is not a mention. \n if the element[i][j] equals to 1, this subtokens from $i$ to $j$ is a mention. \n \"\"\"\n text_len = tf.math.maximum(pad_text_len, tf.zeros_like(pad_text_len, tf.int32)) # (num_of_non_empty_window)\n num_subtoken_in_doc = tf.math.reduce_sum(text_len) # the value should be num_subtoken_in_doc \n \n gold_start_end_mask = tf.cast(tf.math.greater_equal(pad_gold_start_index_labels, tf.zeros_like(pad_gold_start_index_labels, tf.int32)), tf.bool) # (max_num_mention)\n gold_start_index_labels = self.boolean_mask_1d(pad_gold_start_index_labels, gold_start_end_mask, name_scope=\"gold_starts\", use_tpu=self.use_tpu) # (num_of_mention)\n gold_end_index_labels = self.boolean_mask_1d(pad_gold_end_index_labels, gold_start_end_mask, name_scope=\"gold_ends\", use_tpu=self.use_tpu) # (num_of_mention)\n\n gold_start_sequence_labels = self.scatter_gold_index_to_label_sequence(gold_start_index_labels, num_subtoken_in_doc) # (num_subtoken_in_doc)\n gold_end_sequence_labels = self.scatter_gold_index_to_label_sequence(gold_end_index_labels, num_subtoken_in_doc) # (num_subtoken_in_doc)\n gold_span_sequence_labels = self.scatter_span_sequence_labels(gold_start_index_labels, gold_end_index_labels, num_subtoken_in_doc) # (num_subtoken_in_doc, num_subtoken_in_doc)\n\n return gold_start_sequence_labels, gold_end_sequence_labels, gold_span_sequence_labels\n\n\n def scatter_gold_index_to_label_sequence(self, gold_index_labels, expect_length_of_labels):\n \"\"\"\n Desc:\n transform the mention start/end position index tf.int32 Tensor to a tf.int32 Tensor with 1/0 labels for the input subtoken sequences.\n 1 denotes this subtoken is the start/end for a mention. \n Args:\n gold_index_labels: a tf.int32 Tensor with mention start/end position index in the original document. \n expect_length_of_labels: the number of subtokens in the original document. \n \"\"\"\n gold_labels_pos = tf.reshape(gold_index_labels, [-1, 1]) # (num_of_mention, 1)\n gold_value = tf.reshape(tf.ones_like(gold_index_labels), [-1]) # (num_of_mention)\n label_shape = tf.Variable(expect_length_of_labels) \n label_shape = tf.reshape(label_shape, [1]) # [1]\n gold_sequence_labels = tf.cast(tf.scatter_nd(gold_labels_pos, gold_value, label_shape), tf.int32) # (num_subtoken_in_doc)\n return gold_sequence_labels\n\n\n def scatter_span_sequence_labels(self, gold_start_index_labels, gold_end_index_labels, expect_length_of_labels):\n \"\"\"\n Desc:\n transform the mention (start, end) position pairs to a span matrix gold_span_sequence_labels. \n matrix[i][j]: whether the subtokens between the position $i$ to $j$ can be a mention. \n if matrix[i][j] == 0: from $i$ to $j$ is not a mention. \n if matrix[i][j] == 1: from $i$ to $j$ is a mention.\n Args:\n gold_start_index_labels: a tf.int32 Tensor with mention start position index in the original document. \n gold_end_index_labels: a tf.int32 Tensor with mention end position index in the original document. \n expect_length_of_labels: a scalar, should be the same with num_subtoken_in_doc\n \"\"\" \n gold_span_index_labels = tf.stack([gold_start_index_labels, gold_end_index_labels], axis=1) # (num_of_mention, 2)\n gold_span_value = tf.reshape(tf.ones_like(gold_start_index_labels, tf.int32), [-1]) # (num_of_mention)\n gold_span_label_shape = tf.Variable([expect_length_of_labels, expect_length_of_labels]) \n gold_span_label_shape = tf.reshape(gold_span_label_shape, [-1])\n\n gold_span_sequence_labels = tf.cast(tf.scatter_nd(gold_span_index_labels, gold_span_value, gold_span_label_shape), tf.int32) # (num_subtoken_in_doc, num_subtoken_in_doc)\n return gold_span_sequence_labels\n\n\n def compute_score_and_loss(self, pred_sequence_logits, gold_sequence_labels, loss_mask=None):\n \"\"\"\n Desc:\n compute the unifrom start/end loss and probabilities. \n Args:\n pred_sequence_logits: (input_shape, 1) \n gold_sequence_labels: (input_shape, 1)\n loss_mask: [optional] if is not None, it should be (input_shape). should be tf.int32 0/1 tensor. \n FOR start/end score and loss, input_shape should be num_subtoken_in_doc.\n FOR span score and loss, input_shape should be num_subtoken_in_doc * num_subtoken_in_doc. \n \"\"\"\n pred_sequence_probabilities = tf.cast(tf.reshape(tf.sigmoid(pred_sequence_logits), [-1]),tf.float32) # (input_shape)\n expand_pred_sequence_scores = tf.stack([(1 - pred_sequence_probabilities), pred_sequence_probabilities], axis=-1) # (input_shape, 2)\n expand_gold_sequence_labels = tf.cast(tf.one_hot(tf.reshape(gold_sequence_labels, [-1]), 2, axis=-1), tf.float32) # (input_shape, 2)\n\n loss = tf.keras.losses.binary_crossentropy(expand_gold_sequence_labels, expand_pred_sequence_scores)\n # loss -> shape is (input_shape)\n\n if loss_mask is not None:\n loss = tf.multiply(loss, tf.cast(loss_mask, tf.float32))\n\n total_loss = tf.reduce_mean(loss)\n # total_loss -> a scalar \n\n return total_loss, pred_sequence_probabilities\n\n\n def transform_overlap_windows_to_original_doc(self, doc_overlap_window_embs, doc_overlap_input_mask):\n \"\"\"\n Desc:\n hidden_size should be equal to embeddding_size. \n Args:\n doc_overlap_window_embs: (num_window, window_size, hidden_size). \n the output of (num_window, window_size) input_ids forward into BERT model. \n doc_overlap_input_mask: (num_window, window_size). A tf.int32 Tensor contains 0/1. \n 0 represents token in this position should be neglected. 1 represents token in this position should be reserved. \n \"\"\"\n ones_input_mask = tf.ones_like(doc_overlap_input_mask, tf.int32) # (num_window, window_size)\n cumsum_input_mask = tf.math.cumsum(ones_input_mask, axis=1) # (num_window, window_size)\n offset_input_mask = tf.tile(tf.expand_dims(tf.range(self.config.num_window) * self.config.window_size, 1), [1, self.config.window_size]) # (num_window, window_size)\n offset_cumsum_input_mask = offset_input_mask + cumsum_input_mask # (num_window, window_size)\n global_input_mask = tf.math.multiply(ones_input_mask, offset_cumsum_input_mask) # (num_window, window_size)\n global_input_mask = tf.reshape(global_input_mask, [-1]) # (num_window * window_size)\n global_input_mask_index = self.boolean_mask_1d(global_input_mask, tf.math.greater(global_input_mask, tf.zeros_like(global_input_mask, tf.int32))) # (num_subtoken_in_doc)\n\n doc_overlap_window_embs = tf.reshape(doc_overlap_window_embs, [-1, self.config.hidden_size]) # (num_window * window_size, hidden_size)\n original_doc_embs = tf.gather(doc_overlap_window_embs, global_input_mask_index) # (num_subtoken_in_doc, hidden_size)\n\n return original_doc_embs \n\n\n def ffnn(self, inputs, hidden_size, output_size, dropout=None, name_scope=\"fully-conntected-neural-network\",\n hidden_initializer=tf.truncated_normal_initializer(stddev=0.02)):\n \"\"\"\n Desc:\n fully-connected neural network. \n transform non-linearly the [input] tensor with [hidden_size] to a fix [output_size] size. \n Args: \n hidden_size: should be the size of last dimension of [inputs]. \n \"\"\"\n with tf.variable_scope(name_scope, reuse=tf.AUTO_REUSE):\n hidden_weights = tf.get_variable(\"hidden_weights\", [hidden_size, output_size],\n initializer=hidden_initializer)\n hidden_bias = tf.get_variable(\"hidden_bias\", [output_size], initializer=tf.zeros_initializer())\n outputs = tf.nn.relu(tf.nn.xw_plus_b(inputs, hidden_weights, hidden_bias))\n\n if dropout is not None:\n outputs = tf.nn.dropout(outputs, dropout)\n\n return outputs \n\n\n def get_dropout(self, dropout_rate, is_training):\n return 1 - (tf.to_float(is_training) * dropout_rate)\n\n\n def get_shape(self, x, dim):\n \"\"\"\n Desc:\n return the size of input x in DIM. \n \"\"\" \n return x.get_shape()[dim].value or tf.shape(x)[dim]\n\n\n def boolean_mask_1d(self, itemtensor, boolmask_indicator, name_scope=\"boolean_mask1d\", use_tpu=False):\n \"\"\"\n Desc:\n the same functionality of tf.boolean_mask. \n The tf.boolean_mask operation is not available on the cloud TPU. \n Args:\n itemtensor : a Tensor contains [tf.int32, tf.float32] numbers. Should be 1-Rank.\n boolmask_indicator : a tf.bool Tensor. Should be 1-Rank. \n scope : name scope for the operation. \n use_tpu : if False, return tf.boolean_mask. \n \"\"\"\n with tf.name_scope(name_scope):\n if not use_tpu:\n return tf.boolean_mask(itemtensor, boolmask_indicator)\n\n boolmask_sum = tf.reduce_sum(tf.cast(boolmask_indicator, tf.int32))\n selected_positions = tf.cast(boolmask_indicator, dtype=tf.float32)\n indexed_positions = tf.cast(tf.multiply(tf.cumsum(selected_positions), selected_positions),dtype=tf.int32)\n one_hot_selector = tf.one_hot(indexed_positions - 1, boolmask_sum, dtype=tf.float32)\n sampled_indices = tf.cast(tf.tensordot(tf.cast(tf.range(tf.shape(boolmask_indicator)[0]), dtype=tf.float32),\n one_hot_selector,axes=[0, 0]),dtype=tf.int32)\n sampled_indices = tf.reshape(sampled_indices, [-1])\n mask_itemtensor = tf.gather(itemtensor, sampled_indices)\n\n return mask_itemtensor\n\n\n\n\n\n\n\n"
},
{
"path": "requirements.txt",
"content": "pyhocon\ntensorboard==1.15.0\ntensorflow-estimator==1.15.1\ntensorflow-gpu==1.15.0\npyyaml==5.2\nscikit-learn==0.19.1\nscipy\ntorch==1.2.0\n"
},
{
"path": "run/build_dataset_to_tfrecord.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li\n# description:\n# generate tfrecord for train/dev/test set for the model. \n# TODO (xiaoya): need to add help description for args \n\n\n\nimport os\nimport sys \nimport re\nimport json \nimport argparse \nimport numpy as np \nimport tensorflow as tf\nfrom collections import OrderedDict\n\nREPO_PATH = \"/\".join(os.path.realpath(__file__).split(\"/\")[:-2])\nif REPO_PATH not in sys.path:\n sys.path.insert(0, REPO_PATH)\n\nfrom data_utils import conll\nfrom bert.tokenization import FullTokenizer\n\nSPEAKER_START = '[unused19]'\nSPEAKER_END = '[unused73]'\nsubtoken_maps = {}\ngold = {}\n\n\n\n\"\"\"\nDesc:\na single training/test example for the squad dataset.\nsuppose origin input_tokens are :\n['[unused19]', 'speaker', '#', '1', '[unused73]', '-', '-', 'basically', ',', 'it', 'was', 'unanimously', 'agreed', 'upon', 'by', 'the', 'various', 'relevant', 'parties', '.', \n'To', 'express', 'its', 'determination', ',', 'the', 'Chinese', 'securities', 'regulatory', 'department', 'compares', 'this', 'stock', 'reform', 'to', 'a', 'die', 'that', \n'has', 'been', 'cast', '.', 'It', 'takes', 'time', 'to', 'prove', 'whether', 'the', 'stock', 'reform', 'can', 'really', 'meet', 'expectations', ',', 'and', 'whether', 'any', \n'de', '##viation', '##s', 'that', 'arise', 'during', 'the', 'stock', 'reform', 'can', 'be', 'promptly', 'corrected', '.', '[unused19]', 'Xu', '_', 'l', '##i', '[unused73]', \n'Dear', 'viewers', ',', 'the', 'China', 'News', 'program', 'will', 'end', 'here', '.', 'This', 'is', 'Xu', 'Li', '.', 'Thank', 'you', 'everyone', 'for', 'watching', '.', 'Coming', \n'up', 'is', 'the', 'Focus', 'Today', 'program', 'hosted', 'by', 'Wang', 'Shi', '##lin', '.', 'Good', '-', 'bye', ',', 'dear', 'viewers', '.'] \nIF sliding window size is 50. \nArgs:\ndoc_idx: a string: cctv/bn/0001\nsentence_map: \n e.g. [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7]\nsubtoken_map: \n e.g. [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 53, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 97, 98, 99, 99, 99, 100, 101, 102, 103]\nflattened_window_input_ids: [num-window, window-size]\n e.g. before bert_tokenizer convert subtokens into ids:\n [['[CLS]', '[unused19]', 'speaker', '#', '1', '[unused73]', '-', '-', 'basically', ',', 'it', 'was', 'unanimously', 'agreed', 'upon', 'by', 'the', 'various', 'relevant', 'parties', '.', 'To', 'express', 'its', 'determination', ',', 'the', 'Chinese', 'securities', 'regulatory', 'department', 'compares', 'this', 'stock', 'reform', 'to', 'a', 'die', 'that', 'has', 'been', 'cast', '.', 'It', 'takes', 'time', 'to', 'prove', 'whether', '[SEP]'],\n ['[CLS]', ',', 'the', 'Chinese', 'securities', 'regulatory', 'department', 'compares', 'this', 'stock', 'reform', 'to', 'a', 'die', 'that', 'has', 'been', 'cast', '.', 'It', 'takes', 'time', 'to', 'prove', 'whether', 'the', 'stock', 'reform', 'can', 'really', 'meet', 'expectations', ',', 'and', 'whether', 'any', 'de', '##viation', '##s', 'that', 'arise', 'during', 'the', 'stock', 'reform', 'can', 'be', 'promptly', 'corrected', '[SEP]'],\n ['[CLS]', 'the', 'stock', 'reform', 'can', 'really', 'meet', 'expectations', ',', 'and', 'whether', 'any', 'de', '##viation', '##s', 'that', 'arise', 'during', 'the', 'stock', 'reform', 'can', 'be', 'promptly', 'corrected', '.', '[unused19]', 'Xu', '_', 'l', '##i', '[unused73]', 'Dear', 'viewers', ',', 'the', 'China', 'News', 'program', 'will', 'end', 'here', '.', 'This', 'is', 'Xu', 'Li', '.', 'Thank', '[SEP]'],\n ['[CLS]', '.', '[unused19]', 'Xu', '_', 'l', '##i', '[unused73]', 'Dear', 'viewers', ',', 'the', 'China', 'News', 'program', 'will', 'end', 'here', '.', 'This', 'is', 'Xu', 'Li', '.', 'Thank', 'you', 'everyone', 'for', 'watching', '.', 'Coming', 'up', 'is', 'the', 'Focus', 'Today', 'program', 'hosted', 'by', 'Wang', 'Shi', '##lin', '.', 'Good', '-', 'bye', ',', 'dear', 'viewers', '[SEP]'],\n ['[CLS]', 'you', 'everyone', 'for', 'watching', '.', 'Coming', 'up', 'is', 'the', 'Focus', 'Today', 'program', 'hosted', 'by', 'Wang', 'Shi', '##lin', '.', 'Good', '-', 'bye', ',', 'dear', 'viewers', '.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']] \nflattened_window_masked_ids: [num-window, window-size]\n e.g.: before bert_tokenizer ids:\n [[-3, -1, -1, -1, -1, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -3],\n [-3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -3],\n [-3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, -1, -1, -1, -1, -1, -1, 68, 69, 70, 71, 72, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -3],\n [-3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -3],\n [-3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, -3, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4, -4]]\nspan_start: \n e.g.: mention start indices in the original document \n [17, 20, 26, 43, 60, 85, 86]\nspan_end:\n e.g.: mention end indices in the original document \ncluster_ids: \n e.g.: cluster ids for the (span_start, span_end) pairs\n [1, 1, 2, 2, 2, 3, 3] \ncheck the mention in the subword list: \n 1. ['its']\n 1. ['the', 'Chinese', 'securities', 'regulatory', 'department']\n 2. ['this', 'stock', 'reform']\n 2. ['the', 'stock', 'reform']\n 2. ['the', 'stock', 'reform']\n 3. ['you']\n 3. ['everyone']\n\"\"\"\n\n\n\ndef prepare_train_dataset(input_file, output_data_dir, output_filename, window_size, num_window, \n tokenizer=None, vocab_file=None, language=\"english\", max_doc_length=None, genres=None, \n max_num_mention=10, max_num_cluster=30, demo=False, lowercase=False):\n\n if vocab_file is None:\n if not lowercase:\n vocab_file = os.path.join(REPO_PATH, \"data_utils\", \"uppercase_vocab.txt\")\n else:\n vocab_file = os.path.join(REPO_PATH, \"data_utils\", \"lowercase_vocab.txt\")\n\n if tokenizer is None:\n tokenizer = FullTokenizer(vocab_file=vocab_file, do_lower_case=lowercase)\n\n writer = tf.python_io.TFRecordWriter(os.path.join(output_data_dir, \"{}.{}.tfrecord\".format(output_filename, language)))\n doc_map = {}\n documents = read_conll_file(input_file)\n for doc_idx, document in enumerate(documents):\n doc_info = parse_document(document, language)\n tokenized_document = tokenize_document(genres, doc_info, tokenizer)\n doc_key = tokenized_document['doc_key']\n token_windows, mask_windows, text_len = convert_to_sliding_window(tokenized_document, window_size)\n input_id_windows = [tokenizer.convert_tokens_to_ids(tokens) for tokens in token_windows]\n span_start, span_end, mention_span, cluster_ids = flatten_clusters(tokenized_document['clusters'])\n\n tmp_speaker_ids = tokenized_document[\"speakers\"] \n tmp_speaker_ids = [[0]*130]* num_window\n instance = (input_id_windows, mask_windows, text_len, tmp_speaker_ids, tokenized_document[\"genre\"], span_start, span_end, cluster_ids, tokenized_document['sentence_map']) \n write_instance_to_example_file(writer, instance, doc_key, window_size=window_size, num_window=num_window, \n max_num_mention=max_num_mention, max_num_cluster=max_num_cluster)\n doc_map[doc_idx] = doc_key\n if demo and doc_idx > 3:\n break \n with open(os.path.join(output_data_dir, \"{}.{}.map\".format(output_filename, language)), 'w') as fo:\n json.dump(doc_map, fo, indent=2)\n\n\n\ndef write_instance_to_example_file(writer, instance, doc_key, window_size=64, num_window=5, max_num_mention=20,\n max_num_cluster=30, pad_idx=-1):\n\n input_ids, input_mask, text_len, speaker_ids, genre, gold_starts, gold_ends, cluster_ids, sentence_map = instance \n input_id_windows = input_ids \n mask_windows = input_mask \n flattened_input_ids = [i for j in input_id_windows for i in j]\n flattened_input_mask = [i for j in mask_windows for i in j]\n cluster_ids = [int(tmp) for tmp in cluster_ids]\n\n max_sequence_len = int(num_window)\n max_seg_len = int(window_size)\n\n sentence_map = clip_or_pad(sentence_map, max_sequence_len*max_seg_len, pad_idx=pad_idx)\n text_len = clip_or_pad(text_len, max_sequence_len, pad_idx=pad_idx)\n tmp_subtoken_maps = clip_or_pad(subtoken_maps[doc_key], max_sequence_len*max_seg_len, pad_idx=pad_idx)\n\n tmp_speaker_ids = clip_or_pad(speaker_ids[0], max_sequence_len*max_seg_len, pad_idx=pad_idx)\n\n flattened_input_ids = clip_or_pad(flattened_input_ids, max_sequence_len*max_seg_len, pad_idx=pad_idx)\n flattened_input_mask = clip_or_pad(flattened_input_mask, max_sequence_len*max_seg_len, pad_idx=pad_idx)\n gold_starts = clip_or_pad(gold_starts, max_num_mention, pad_idx=pad_idx)\n gold_ends = clip_or_pad(gold_ends, max_num_mention, pad_idx=pad_idx)\n cluster_ids = clip_or_pad(cluster_ids, max_num_cluster, pad_idx=pad_idx)\n\n features = OrderedDict()\n features['sentence_map'] = create_int_feature(sentence_map)\n features['text_len'] = create_int_feature(text_len)\n features['subtoken_map'] = create_int_feature(tmp_subtoken_maps)\n features['speaker_ids'] = create_int_feature(tmp_speaker_ids)\n features['flattened_input_ids'] = create_int_feature(flattened_input_ids)\n features['flattened_input_mask'] = create_int_feature(flattened_input_mask)\n features['span_starts'] = create_int_feature(gold_starts)\n features['span_ends'] = create_int_feature(gold_ends)\n features['cluster_ids'] = create_int_feature(cluster_ids)\n\n tf_example = tf.train.Example(features=tf.train.Features(feature=features))\n writer.write(tf_example.SerializeToString())\n\n\ndef create_int_feature(values):\n feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))\n return feature\n\n\ndef clip_or_pad(var, max_var_len, pad_idx=-1):\n \n if len(var) >= max_var_len:\n return var[:max_var_len]\n else:\n pad_var = (max_var_len - len(var)) * [pad_idx]\n var = list(var) + list(pad_var) \n return var \n\n\ndef flatten_clusters(clusters):\n\n span_starts = []\n span_ends = []\n cluster_ids = []\n mention_span = []\n for cluster_id, cluster in enumerate(clusters):\n for start, end in cluster:\n span_starts.append(start)\n span_ends.append(end)\n mention_span.append((start, end))\n cluster_ids.append(cluster_id + 1)\n return span_starts, span_ends, mention_span, cluster_ids\n\n\ndef read_conll_file(conll_file_path):\n documents = []\n with open(conll_file_path, \"r\", encoding=\"utf-8\") as fi:\n for line in fi:\n begin_document_match = re.match(conll.BEGIN_DOCUMENT_REGEX, line)\n if begin_document_match:\n doc_key = conll.get_doc_key(begin_document_match.group(1), begin_document_match.group(2))\n documents.append((doc_key, []))\n elif line.startswith(\"#end document\"):\n continue\n else:\n documents[-1][1].append(line.strip())\n return documents\n\n\ndef parse_document(document, language):\n \"\"\"\n get basic information from one document annotation.\n :param document:\n :param language: english, chinese or arabic\n :return:\n \"\"\"\n doc_key = document[0]\n sentences = [[]]\n speakers = []\n coreferences = []\n word_idx = -1\n last_speaker = ''\n for line_id, line in enumerate(document[1]):\n row = line.split()\n sentence_end = len(row) == 0\n if not sentence_end:\n assert len(row) >= 12\n word_idx += 1\n word = normalize_word(row[3], language)\n sentences[-1].append(word)\n speaker = row[9]\n if speaker != last_speaker:\n speakers.append((word_idx, speaker))\n last_speaker = speaker\n coreferences.append(row[-1])\n else:\n sentences.append([])\n clusters = coreference_annotations_to_clusters(coreferences)\n doc_info = {'doc_key': doc_key, 'sentences': sentences[: -1], 'speakers': speakers, 'clusters': clusters}\n return doc_info\n\n\ndef normalize_word(word, language):\n if language == \"arabic\":\n word = word[:word.find(\"#\")]\n if word == \"/.\" or word == \"/?\":\n return word[1:]\n else:\n return word\n\n\ndef coreference_annotations_to_clusters(annotations):\n \"\"\"\n convert coreference information to clusters\n :param annotations:\n :return:\n \"\"\"\n clusters = OrderedDict()\n coref_stack = OrderedDict()\n for word_idx, annotation in enumerate(annotations):\n if annotation == '-':\n continue\n for ann in annotation.split('|'):\n cluster_id = int(ann.replace('(', '').replace(')', ''))\n if ann[0] == '(' and ann[-1] == ')':\n if cluster_id not in clusters.keys():\n clusters[cluster_id] = [(word_idx, word_idx)]\n else:\n clusters[cluster_id].append((word_idx, word_idx))\n elif ann[0] == '(':\n if cluster_id not in coref_stack.keys():\n coref_stack[cluster_id] = [word_idx]\n else:\n coref_stack[cluster_id].append(word_idx)\n elif ann[-1] == ')':\n span_start = coref_stack[cluster_id].pop()\n if cluster_id not in clusters.keys():\n clusters[cluster_id] = [(span_start, word_idx)]\n else:\n clusters[cluster_id].append((span_start, word_idx))\n else:\n raise NotImplementedError\n assert all([len(starts) == 0 for starts in coref_stack.values()])\n return list(clusters.values())\n\n\ndef checkout_clusters(doc_info):\n words = [i for j in doc_info['sentences'] for i in j]\n clusters = [[' '.join(words[start: end + 1]) for start, end in cluster] for cluster in doc_info['clusters']]\n print(clusters)\n\n\ndef tokenize_document(genres, doc_info, tokenizer):\n \"\"\"\n tokenize into sub tokens\n :param doc_info:\n :param tokenizer:\n max_doc_length: pad to max_doc_length\n :return:\n \"\"\"\n genres = {g: i for i, g in enumerate(genres)}\n sub_tokens = [] # all sub tokens of a document\n sentence_map = [] # collected tokenized tokens -> sentence id\n subtoken_map = [] # collected tokenized tokens -> original token id\n\n word_idx = -1\n\n for sentence_id, sentence in enumerate(doc_info['sentences']):\n for token in sentence:\n word_idx += 1\n word_tokens = tokenizer.tokenize(token)\n sub_tokens.extend(word_tokens)\n sentence_map.extend([sentence_id] * len(word_tokens))\n subtoken_map.extend([word_idx] * len(word_tokens))\n \n\n subtoken_maps[doc_info['doc_key']] = subtoken_map\n genre = genres.get(doc_info['doc_key'][:2], 0)\n speakers = {subtoken_map.index(word_index): tokenizer.tokenize(speaker)\n for word_index, speaker in doc_info['speakers']}\n clusters = [[(subtoken_map.index(start), len(subtoken_map) - 1 - subtoken_map[::-1].index(end))\n for start, end in cluster] for cluster in doc_info['clusters']]\n tokenized_document = {'sub_tokens': sub_tokens, 'sentence_map': sentence_map, 'subtoken_map': subtoken_map,\n 'speakers': speakers, 'clusters': clusters, 'doc_key': doc_info['doc_key'], \n \"genre\": genre}\n return tokenized_document\n\n\ndef convert_to_sliding_window(tokenized_document, sliding_window_size):\n \"\"\"\n construct sliding windows, allocate tokens and masks into each window\n :param tokenized_document:\n :param sliding_window_size:\n :return:\n \"\"\"\n expanded_tokens, expanded_masks = expand_with_speakers(tokenized_document)\n sliding_windows = construct_sliding_windows(len(expanded_tokens), sliding_window_size - 2)\n token_windows = [] # expanded tokens to sliding window\n mask_windows = [] # expanded masks to sliding window\n text_len = []\n\n for window_start, window_end, window_mask in sliding_windows:\n original_tokens = expanded_tokens[window_start: window_end]\n original_masks = expanded_masks[window_start: window_end]\n window_masks = [-2 if w == 0 else o for w, o in zip(window_mask, original_masks)]\n one_window_token = ['[CLS]'] + original_tokens + ['[SEP]'] + ['[PAD]'] * (\n sliding_window_size - 2 - len(original_tokens))\n one_window_mask = [-3] + window_masks + [-3] + [-4] * (sliding_window_size - 2 - len(original_tokens))\n token_calculate = [tmp for tmp in one_window_mask if tmp >= 0]\n text_len.append(len(token_calculate))\n assert len(one_window_token) == sliding_window_size\n assert len(one_window_mask) == sliding_window_size\n token_windows.append(one_window_token)\n mask_windows.append(one_window_mask)\n assert len(tokenized_document['sentence_map']) == sum([i >= 0 for j in mask_windows for i in j])\n\n text_len = np.array(text_len)\n return token_windows, mask_windows, text_len\n\n\ndef expand_with_speakers(tokenized_document):\n \"\"\"\n add speaker name information\n :param tokenized_document: tokenized document information\n :return:\n \"\"\"\n expanded_tokens = []\n expanded_masks = []\n for token_idx, token in enumerate(tokenized_document['sub_tokens']):\n if token_idx in tokenized_document['speakers']:\n speaker = [SPEAKER_START] + tokenized_document['speakers'][token_idx] + [SPEAKER_END]\n expanded_tokens.extend(speaker)\n expanded_masks.extend([-1] * len(speaker))\n expanded_tokens.append(token)\n expanded_masks.append(token_idx)\n return expanded_tokens, expanded_masks\n\n\ndef construct_sliding_windows(sequence_length, sliding_window_size):\n \"\"\"\n construct sliding windows for BERT processing\n :param sequence_length: e.g. 9\n :param sliding_window_size: e.g. 4\n :return: [(0, 4, [1, 1, 1, 0]), (2, 6, [0, 1, 1, 0]), (4, 8, [0, 1, 1, 0]), (6, 9, [0, 1, 1])]\n \"\"\"\n sliding_windows = []\n stride = int(sliding_window_size / 2)\n start_index = 0\n end_index = 0\n while end_index < sequence_length:\n end_index = min(start_index + sliding_window_size, sequence_length)\n left_value = 1 if start_index == 0 else 0\n right_value = 1 if end_index == sequence_length else 0\n mask = [left_value] * int(sliding_window_size / 4) + [1] * int(sliding_window_size / 2) \\\n + [right_value] * (sliding_window_size - int(sliding_window_size / 2) - int(sliding_window_size / 4))\n mask = mask[: end_index - start_index]\n sliding_windows.append((start_index, end_index, mask))\n start_index += stride\n assert sum([sum(window[2]) for window in sliding_windows]) == sequence_length\n return sliding_windows\n\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--source_files_dir\", default=\"/home/lixiaoya/data\", type=str, required=True)\n parser.add_argument(\"--target_output_dir\", default=\"/home/lixiaoya/tfrecord_data\", type=str, required=True)\n parser.add_argument(\"--num_window\", default=5, type=int, required=True)\n parser.add_argument(\"--window_size\", default=64, type=int, required=True)\n parser.add_argument(\"--max_num_mention\", default=30, type=int)\n parser.add_argument(\"--max_num_cluster\", default=20, type=int)\n parser.add_argument(\"--vocab_file\", default=\"/home/lixiaoya/spanbert_large_cased/vocab.txt\", type=str)\n parser.add_argument(\"--language\", default=\"english\", type=str)\n parser.add_argument(\"--max_doc_length\", default=600, type=int)\n parser.add_argument(\"--lowercase\", help=\"DO or NOT lowercase the datasets.\", action=\"store_true\")\n parser.add_argument(\"--demo\", help=\"Wether to generate a small dataset for testing the code.\", action=\"store_true\")\n parser.add_argument('--genres', default=[\"bc\",\"bn\",\"mz\",\"nw\",\"pt\",\"tc\",\"wb\"])\n parser.add_argument(\"--seed\", default=2333, type=int)\n\n args = parser.parse_args()\n\n os.makedirs(args.target_output_dir, exist_ok=True)\n np.random.seed(args.seed)\n tf.set_random_seed(args.seed)\n\n return args\n\n\ndef main():\n args_config = parse_args()\n\n print(\"*\"*60)\n print(\"***** ***** show configs ***** ***** \")\n print(\"window_size : {}\".format(str(args_config.window_size)))\n print(\"num_window : {}\".format(str(args_config.num_window)))\n print(\"*\"*60)\n\n for data_sign in [\"train\", \"dev\", \"test\"]:\n source_data_file = os.path.join(args_config.source_files_dir, \"{}.{}.v4_gold_conll\".format(data_sign, args_config.language))\n output_filename = \"{}.overlap.corefqa\".format(data_sign)\n \n if args_config.demo:\n if args_config.lowercase:\n output_filename=\"demo.lowercase.{}.overlap.corefqa\".format(data_sign)\n else:\n output_filename=\"demo.{}.overlap.corefqa\".format(data_sign)\n\n print(\"$\"*60)\n print(\"generate {}/{}\".format(args_config.target_output_dir, output_filename))\n prepare_train_dataset(source_data_file, args_config.target_output_dir, output_filename, args_config.window_size, \n args_config.num_window, vocab_file=args_config.vocab_file, language=args_config.language, \n max_doc_length=args_config.max_doc_length, genres=args_config.genres, max_num_mention=args_config.max_num_mention,\n max_num_cluster=args_config.max_num_cluster, demo=args_config.demo, lowercase=args_config.lowercase)\n\n\n\n\nif __name__ == \"__main__\":\n main()\n\n # please refer ${REPO_PATH}/scripts/data/generate_tfrecord_dataset.sh \n # \n # for generate tfrecord datasets \n # \n # python3 build_dataset_to_tfrecord.py \\\n # --source_files_dir /xiaoya/data \\\n # --target_output_dir /xiaoya/corefqa_data/overlap_64_2 \\\n # --num_window 2 \\\n # --window_size 64 \\\n # --max_num_mention 50 \\\n # --max_num_cluster 30 \\\n # --vocab_file /xiaoya/pretrain_ckpt/cased_L-12_H-768_A-12/vocab.txt \\\n # --language english \\\n # --max_doc_length 600 \n # \n\n\n\n"
},
{
"path": "run/run_corefqa.py",
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*- \n\n\"\"\"\nthis file contains training and testing the CorefQA model. \n\"\"\"\n\nimport os \nimport math \nimport logging\nimport random \nimport numpy as np \nimport tensorflow as tf\nfrom utils import util\nfrom utils import metrics\nfrom data_utils.config_utils import ModelConfig\nfrom func_builders.model_fn_builder import model_fn_builder \nfrom func_builders.input_fn_builder import file_based_input_fn_builder\n\n\ntf.app.flags.DEFINE_string('f', '', 'kernel')\nflags = tf.app.flags\n\nflags.DEFINE_string(\"output_dir\", \"data\", \"The output directory.\")\nflags.DEFINE_string(\"bert_config_file\", \"/home/uncased_L-2_H-128_A-2/config.json\", \"The config json file corresponding to the pre-trained BERT model.\")\nflags.DEFINE_string(\"init_checkpoint\", \"/home/uncased_L-2_H-128_A-2/bert_model.ckpt\", \"Initial checkpoint (usually from a pre-trained BERT model).\")\nflags.DEFINE_string(\"vocab_file\", \"/home/uncased_L-2_H-128_A-2/vocab.txt\", \"The vocabulary file that the BERT model was trained on.\")\nflags.DEFINE_string(\"logfile_path\", \"/home/lixiaoya/spanbert_large_mention_proposal.log\", \"the path to the exported log file.\")\nflags.DEFINE_integer(\"num_epochs\", 20, \"Total number of training epochs to perform.\")\nflags.DEFINE_integer(\"keep_checkpoint_max\", 30, \"How many checkpoint models keep at most.\")\nflags.DEFINE_integer(\"save_checkpoints_steps\", 500, \"Save checkpoint every X updates steps.\")\n\n\nflags.DEFINE_string(\"train_file\", \"/home/lixiaoya/train.english.tfrecord\", \"TFRecord file for training. E.g., train.english.tfrecord\")\nflags.DEFINE_string(\"dev_file\", \"/home/lixiaoya/dev.english.tfrecord\", \"TFRecord file for validating. E.g., dev.english.tfrecord\")\nflags.DEFINE_string(\"test_file\", \"/home/lixiaoya/test.english.tfrecord\", \"TFRecord file for testing. E.g., test.english.tfrecord\")\n\n\nflags.DEFINE_bool(\"do_train\", True, \"Whether to train a model.\")\nflags.DEFINE_bool(\"do_eval\", False, \"Whether to do evaluation: evaluation is done on a set of trained checkpoints, the model will select the best one on the dev set, and report result on the test set\")\nflags.DEFINE_bool(\"do_predict\", False, \"Whether to test (only) one trained model.\")\nflags.DEFINE_string(\"eval_checkpoint\", \"/home/lixiaoya/mention_proposal_output_dir/bert_model.ckpt\", \"[Optional] The saved checkpoint for evaluation (usually after the training process).\")\nflags.DEFINE_integer(\"iterations_per_loop\", 1000, \"How many steps to make in each estimator call.\")\n\n\nflags.DEFINE_float(\"learning_rate\", 3e-5, \"The initial learning rate for Adam.\")\nflags.DEFINE_float(\"dropout_rate\", 0.3, \"Dropout rate for the training process.\")\nflags.DEFINE_float(\"mention_threshold\", 0.5, \"The threshold for determining whether the span is a mention.\")\nflags.DEFINE_integer(\"hidden_size\", 128, \"The size of hidden layers for the pre-trained model.\")\nflags.DEFINE_integer(\"num_docs\", 5604, \"[Optional] The number of documents in the training files. Only need to change when conduct experiments on the small test sets.\")\nflags.DEFINE_integer(\"window_size\", 384, \"The number of sliding window size. Each document is split into a set of subdocuments with length set to window_size.\")\nflags.DEFINE_integer(\"num_window\", 5, \"The max number of windows for one document. This is used for fitting a document into fix shape for TF computation. \\\n If a document is longer than num_window*window_size, the exceeding part will be abandoned. This only affects training and does not affect test, since the all \\\n docs in the test set is shorter than num_window*window_size\")\nflags.DEFINE_integer(\"max_num_mention\", 30, \"The max number of mentions in one document.\")\nflags.DEFINE_bool(\"start_end_share\", False, \"Whether only to use [start, end] embedding to calculate the start/end scores.\") \nflags.DEFINE_integer(\"max_span_width\", 5, \"The max length of a mention.\")\nflags.DEFINE_integer(\"max_candidate_mentions\", 30, \"The number of candidate mentions.\")\nflags.DEFINE_float(\"top_span_ratio\", 0.2, \"The ratio of.\")\nflags.DEFINE_integer(\"max_top_antecedents\", 30, \"The number of top_antecedents candidate mentions.\")\nflags.DEFINE_integer(\"max_query_len\", 150, \".\")\nflags.DEFINE_integer(\"max_context_len\", 150, \".\")\nflags.DEFINE_bool(\"sec_qa_mention_score\", False, \"Whether to use TPU or GPU/CPU.\")\n\n\nflags.DEFINE_bool(\"use_tpu\", False, \"Whether to use TPU or GPU/CPU.\")\nflags.DEFINE_string(\"tpu_name\", None, \"The Cloud TPU to use for training. This should be either the name used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.\")\nflags.DEFINE_string(\"tpu_zone\", None, \"[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt to automatically detect the GCE project from metadata.\")\nflags.DEFINE_string(\"gcp_project\", None, \"[Optional] Project name for the Cloud TPU-enabled project. If not specified, we will attempt to automatically detect the GCE project from metadata.\")\nflags.DEFINE_string(\"master\", None, \"[Optional] TensorFlow master URL.\")\nflags.DEFINE_integer(\"num_tpu_cores\", 1, \"[Optional] Only used if `use_tpu` is True. Total number of TPU cores to use.\")\nflags.DEFINE_integer(\"seed\", 2333, \"[Optional] Random seed for initialization.\")\n\n\nFLAGS = tf.flags.FLAGS\n\n\nformat = '%(asctime)s - %(levelname)s - %(name)s - %(message)s'\nlogging.basicConfig(format=format, filename=FLAGS.logfile_path, level=logging.INFO)\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n\n\ndef main(_):\n\n tf.logging.set_verbosity(tf.logging.INFO)\n num_train_steps = FLAGS.num_docs * FLAGS.num_epochs\n\n\n keep_chceckpoint_max = max(math.ceil(num_train_steps / FLAGS.save_checkpoints_steps), FLAGS.keep_checkpoint_max)\n\n if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:\n raise ValueError(\"At least one of `do_train`, `do_eval` or `do_predict' must be True.\")\n\n tf.gfile.MakeDirs(FLAGS.output_dir)\n tpu_cluster_resolver = None\n if FLAGS.use_tpu and FLAGS.tpu_name:\n tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(\n FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)\n tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)\n tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)\n\n\n is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2\n run_config = tf.contrib.tpu.RunConfig(\n cluster=tpu_cluster_resolver,\n master=FLAGS.master,\n model_dir=FLAGS.output_dir,\n evaluation_master=FLAGS.master,\n keep_checkpoint_max = keep_chceckpoint_max,\n save_checkpoints_steps=FLAGS.save_checkpoints_steps,\n session_config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True),\n tpu_config=tf.contrib.tpu.TPUConfig(\n iterations_per_loop=FLAGS.iterations_per_loop,\n num_shards=FLAGS.num_tpu_cores,\n per_host_input_for_training=is_per_host))\n\n\n model_config = ModelConfig(FLAGS, FLAGS.output_dir)\n model_config.logging_configs()\n\n\n model_fn = model_fn_builder(model_config, model_sign=\"corefqa\")\n estimator = tf.contrib.tpu.TPUEstimator(\n use_tpu=FLAGS.use_tpu,\n eval_on_tpu=FLAGS.use_tpu,\n warm_start_from=tf.estimator.WarmStartSettings(FLAGS.init_checkpoint,\n vars_to_warm_start=\"bert*\"),\n model_fn=model_fn,\n config=run_config,\n train_batch_size=1,\n eval_batch_size=1,\n predict_batch_size=1)\n\n\n if FLAGS.do_train:\n estimator.train(input_fn=file_based_input_fn_builder(FLAGS.train_file, num_window=FLAGS.num_window,\n window_size=FLAGS.window_size, max_num_mention=FLAGS.max_num_mention, is_training=True, drop_remainder=True), \n max_steps=num_train_steps)\n\n\n if FLAGS.do_eval:\n best_dev_f1, best_dev_prec, best_dev_rec, test_f1_when_dev_best, test_prec_when_dev_best, test_rec_when_dev_best = 0, 0, 0, 0, 0, 0\n best_ckpt_path = \"\"\n checkpoints_iterator = [os.path.join(FLAGS.eval_dir, \"model.ckpt-{}\".format(str(int(ckpt_idx)))) for ckpt_idx in range(0, num_train_steps+1, FLAGS.save_checkpoints_steps)]\n model = util.get_model(model_config, model_sign=\"corefqa\")\n for checkpoint_path in checkpoints_iterator[1:]:\n dev_coref_evaluator = metrics.CorefEvaluator()\n for result in estimator.predict(file_based_input_fn_builder(FLAGS.dev_file, num_window=FLAGS.num_window, \n window_size=FLAGS.window_size, max_num_mention=FLAGS.max_num_mention, is_training=False, drop_remainder=False), \n steps=698, checkpoint_path=checkpoint_path, yield_single_examples=False):\n \n predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold = model.evaluate(result[\"topk_span_starts\"], result[\"topk_span_ends\"], result[\"top_antecedent\"],\n result[\"cluster_ids\"], result[\"gold_starts\"], result[\"gold_ends\"])\n dev_coref_evaluator.update(predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold) \n dev_prec, dev_rec, dev_f1 = dev_coref_evaluator.get_prf()\n tf.logging.info(\"***** Current ckpt path is ***** : {}\".format(checkpoint_path))\n tf.logging.info(\"***** EVAL ON DEV SET *****\")\n tf.logging.info(\"***** [DEV EVAL] ***** : precision: {:.4f}, recall: {:.4f}, f1: {:.4f}\".format(dev_prec, dev_rec, dev_f1))\n if dev_f1 > best_dev_f1:\n best_ckpt_path = checkpoint_path\n best_dev_f1 = dev_f1\n best_dev_prec = dev_prec\n best_dev_rec = dev_rec \n test_coref_evaluator = metrics.CorefEvaluator()\n for result in estimator.predict(file_based_input_fn_builder(FLAGS.test_file, \n num_window=FLAGS.num_window, window_size=FLAGS.window_size, max_num_mention=FLAGS.max_num_mention, \n is_training=False, drop_remainder=False),steps=698, checkpoint_path=checkpoint_path, yield_single_examples=False):\n predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold = model.evaluate(result[\"topk_span_starts\"], result[\"topk_span_ends\"], result[\"top_antecedent\"], \n result[\"cluster_ids\"], result[\"gold_starts\"], result[\"gold_ends\"])\n test_coref_evaluator.update(predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold)\n\n test_pre, test_rec, test_f1 = test_coref_evaluator.get_prf()\n test_f1_when_dev_best, test_prec_when_dev_best, test_rec_when_dev_best = test_f1, test_pre, test_rec \n tf.logging.info(\"***** EVAL ON TEST SET *****\")\n tf.logging.info(\"***** [TEST EVAL] ***** : precision: {:.4f}, recall: {:.4f}, f1: {:.4f}\".format(test_pre, test_rec, test_f1))\n\n tf.logging.info(\"*\"*20)\n tf.logging.info(\"- @@@@@ the path to the BEST DEV result is : {}\".format(best_ckpt_path))\n tf.logging.info(\"- @@@@@ BEST DEV F1 : {:.4f}, Precision : {:.4f}, Recall : {:.4f},\".format(best_dev_f1, best_dev_prec, best_dev_rec))\n tf.logging.info(\"- @@@@@ TEST when DEV best F1 : {:.4f}, Precision : {:.4f}, Recall : {:.4f},\".format(test_f1_when_dev_best, test_prec_when_dev_best, test_rec_when_dev_best))\n\n\n if FLAGS.do_predict:\n coref_evaluator = metrics.CorefEvaluator()\n model = util.get_model(model_config, model_sign=\"corefqa\")\n for result in estimator.predict(file_based_input_fn_builder(FLAGS.test_file, \n num_window=FLAGS.num_window, window_size=FLAGS.window_size, max_num_mention=FLAGS.max_num_mention, \n is_training=False, drop_remainder=False),steps=698, checkpoint_path=checkpoint_path, yield_single_examples=False):\n \n predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold = model.evaluate(result[\"topk_span_starts\"], result[\"topk_span_ends\"], \n result[\"top_antecedent\"], result[\"cluster_ids\"], result[\"gold_starts\"], result[\"gold_ends\"])\n coref_evaluator.update(predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold)\n \n p, r, f = coref_evaluator.get_prf()\n tf.logging.info(\"Average precision: {:.4f}, Average recall: {:.4f}, Average F1 {:.4f}\".format(p, r, f))\n\n\n\nif __name__ == '__main__':\n # set the random seed. \n random.seed(FLAGS.seed)\n np.random.seed(FLAGS.seed)\n tf.set_random_seed(FLAGS.seed)\n # start train/evaluate the model.\n tf.app.run()\n\n\n\n\n\n"
},
{
"path": "run/run_mention_proposal.py",
"content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*- \n\n\n\"\"\"\nthis file contains pre-training and testing the mention proposal model\n\"\"\"\n\nimport os \nimport math \nimport random \nimport logging\nimport numpy as np \nimport tensorflow as tf\nfrom data_utils.config_utils import ModelConfig\nfrom func_builders.model_fn_builder import model_fn_builder \nfrom func_builders.input_fn_builder import file_based_input_fn_builder\nfrom utils.metrics import mention_proposal_prediction\n\ntf.app.flags.DEFINE_string('f', '', 'kernel')\nflags = tf.app.flags\n\nflags.DEFINE_string(\"output_dir\", \"data\", \"The output directory of the model training.\")\nflags.DEFINE_string(\"bert_config_file\", \"/home/uncased_L-2_H-128_A-2/config.json\", \"The config json file corresponding to the pre-trained BERT model.\")\nflags.DEFINE_string(\"init_checkpoint\", \"/home/uncased_L-2_H-128_A-2/bert_model.ckpt\", \"Initial checkpoint (usually from a pre-trained BERT model).\")\nflags.DEFINE_string(\"vocab_file\", \"/home/uncased_L-2_H-128_A-2/vocab.txt\", \"The vocabulary file that the BERT model was trained on.\")\nflags.DEFINE_string(\"logfile_path\", \"/home/lixiaoya/spanbert_large_mention_proposal.log\", \"the path to the exported log file.\")\nflags.DEFINE_integer(\"num_epochs\", 20, \"Total number of training epochs to perform.\")\nflags.DEFINE_integer(\"keep_checkpoint_max\", 30, \"How many checkpoint models keep at most.\")\nflags.DEFINE_integer(\"save_checkpoints_steps\", 500, \"Save checkpoint every X updates steps.\")\n\n\nflags.DEFINE_string(\"train_file\", \"/home/lixiaoya/train.english.tfrecord\", \"TFRecord file for training. E.g., train.english.tfrecord\")\nflags.DEFINE_string(\"dev_file\", \"/home/lixiaoya/dev.english.tfrecord\", \"TFRecord file for validating. E.g., dev.english.tfrecord\")\nflags.DEFINE_string(\"test_file\", \"/home/lixiaoya/test.english.tfrecord\", \"TFRecord file for testing. E.g., test.english.tfrecord\")\n\n\nflags.DEFINE_bool(\"do_train\", True, \"Whether to train a model.\")\nflags.DEFINE_bool(\"do_eval\", False, \"whether to do evaluation: evaluation is done on a set of trained checkpoints, the checkpoint with the best score on the dev set will be selected.\")\nflags.DEFINE_bool(\"do_predict\", False, \"Whether to test (only) one trained model.\")\nflags.DEFINE_string(\"eval_checkpoint\", \"/home/lixiaoya/mention_proposal_output_dir/bert_model.ckpt\", \"[Optional] The saved checkpoint for evaluation (usually after the training process).\")\nflags.DEFINE_integer(\"iterations_per_loop\", 1000, \"How many steps to make in each estimator call.\")\n\n\nflags.DEFINE_float(\"learning_rate\", 3e-5, \"The initial learning rate for Adam.\")\nflags.DEFINE_float(\"dropout_rate\", 0.3, \"Dropout rate for the training process.\")\nflags.DEFINE_float(\"mention_threshold\", 0.5, \"The threshold for determining whether the span is a mention.\")\nflags.DEFINE_integer(\"hidden_size\", 128, \"The size of hidden layers for the pre-trained model.\")\nflags.DEFINE_integer(\"num_docs\", 5604, \"[Optional] The number of documents in the training files. Only need to change when conduct experiments on the small test sets.\")\nflags.DEFINE_integer(\"window_size\", 384, \"The number of sliding window size. Each document is split into a set of subdocuments with length set to window_size.\")\nflags.DEFINE_integer(\"num_window\", 5, \"The max number of windows for one document. This is used for fitting a document into fix shape for TF computation. \\\n If a document is longer than num_window*window_size, the exceeding part will be abandoned. This only affects training and does not affect test, since the all \\\n docs in the test set is shorter than num_window*window_size\")\nflags.DEFINE_integer(\"max_num_mention\", 30, \"The max number of mentions in one document.\")\nflags.DEFINE_bool(\"start_end_share\", False, \"Whether only to use [start, end] embedding to calculate the start/end scores.\") \nflags.DEFINE_float(\"loss_start_ratio\", 0.3, \"As described in the paper, the loss for a span being a mention is -loss_start_ratio* log p(the start of the given span is a start).\")\nflags.DEFINE_float(\"loss_end_ratio\", 0.3, \"As described in the paper, the loss for a span being a mention is -loss_end_ratio* log p(the end of the given span is a end).\")\nflags.DEFINE_float(\"loss_span_ratio\", 0.4, \"As described in the paper, the loss for a span being a mention is -loss_span_ratio* log p(the start and the end forms a span).\")\n\n\nflags.DEFINE_bool(\"use_tpu\", False, \"Whether to use TPU or GPU/CPU.\")\nflags.DEFINE_string(\"tpu_name\", None, \"The Cloud TPU to use for training. This should be either the name used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.\")\nflags.DEFINE_string(\"tpu_zone\", None, \"[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt to automatically detect the GCE project from metadata.\")\nflags.DEFINE_string(\"gcp_project\", None, \"[Optional] Project name for the Cloud TPU-enabled project. If not specified, we will attempt to automatically detect the GCE project from metadata.\")\nflags.DEFINE_string(\"master\", None, \"[Optional] TensorFlow master URL.\")\nflags.DEFINE_integer(\"num_tpu_cores\", 1, \"[Optional] Only used if `use_tpu` is True. Total number of TPU cores to use.\")\nflags.DEFINE_integer(\"seed\", 2333, \"[Optional] Random seed for initialization.\")\nFLAGS = tf.flags.FLAGS\n\n\n\nformat = '%(asctime)s - %(levelname)s - %(name)s - %(message)s'\nlogging.basicConfig(format=format, filename=FLAGS.logfile_path, level=logging.INFO)\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n\n\ndef main(_):\n\n tf.logging.set_verbosity(tf.logging.INFO)\n num_train_steps = FLAGS.num_docs * FLAGS.num_epochs\n # num_train_steps = 100 \n keep_chceckpoint_max = max(math.ceil(num_train_steps / FLAGS.save_checkpoints_steps), FLAGS.keep_checkpoint_max)\n\n if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:\n raise ValueError(\"At least one of `do_train`, `do_eval` or `do_predict' must be True.\")\n\n tf.gfile.MakeDirs(FLAGS.output_dir)\n tpu_cluster_resolver = None\n if FLAGS.use_tpu and FLAGS.tpu_name:\n tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(\n FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)\n tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)\n tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)\n\n is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2\n run_config = tf.contrib.tpu.RunConfig(\n cluster=tpu_cluster_resolver,\n master=FLAGS.master,\n # evaluation_master=FLAGS.master,\n model_dir=FLAGS.output_dir,\n keep_checkpoint_max = keep_chceckpoint_max,\n save_checkpoints_steps=FLAGS.save_checkpoints_steps,\n # session_config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True),\n tpu_config=tf.contrib.tpu.TPUConfig(\n iterations_per_loop=FLAGS.iterations_per_loop,\n num_shards=FLAGS.num_tpu_cores,\n per_host_input_for_training=is_per_host))\n\n\n model_config = ModelConfig(FLAGS, FLAGS.output_dir)\n model_config.logging_configs()\n\n model_fn = model_fn_builder(model_config, model_sign=\"mention_proposal\")\n estimator = tf.contrib.tpu.TPUEstimator(\n use_tpu=FLAGS.use_tpu,\n # eval_on_tpu=FLAGS.use_tpu,\n warm_start_from=tf.estimator.WarmStartSettings(FLAGS.init_checkpoint,\n vars_to_warm_start=\"bert*\"),\n model_fn=model_fn,\n config=run_config,\n train_batch_size=1,\n predict_batch_size=1)\n\n\n if FLAGS.do_train:\n estimator.train(input_fn=file_based_input_fn_builder(model_config.train_file, num_window=model_config.num_window,\n window_size=model_config.window_size, max_num_mention=model_config.max_num_mention, is_training=True, drop_remainder=True), max_steps=num_train_steps)\n\n\n if FLAGS.do_eval:\n # doing evaluation on a set of trained checkpoints, the checkpoint with the best score on the dev set will be selected.\n best_dev_f1, best_dev_prec, best_dev_rec, test_f1_when_dev_best, test_prec_when_dev_best, test_rec_when_dev_best = 0, 0, 0, 0, 0, 0\n best_ckpt_path = \"\"\n checkpoints_iterator = [os.path.join(FLAGS.eval_dir, \"model.ckpt-{}\".format(str(int(ckpt_idx)))) for ckpt_idx in range(0, num_train_steps, FLAGS.save_checkpoints_steps)]\n for checkpoint_path in checkpoints_iterator[1:]:\n eval_dev_result = estimator.evaluate(input_fn=file_based_input_fn_builder(FLAGS.dev_file, num_window=FLAGS.num_window, \n window_size=FLAGS.window_size, max_num_mention=FLAGS.max_num_mention, is_training=False, drop_remainder=False),\n steps=698, checkpoint_path=checkpoint_path)\n dev_f1 = 2*eval_dev_result[\"precision\"] * eval_dev_result[\"recall\"] / (eval_dev_result[\"precision\"] + eval_dev_result[\"recall\"]+1e-10)\n tf.logging.info(\"***** Current ckpt path is ***** : {}\".format(checkpoint_path))\n tf.logging.info(\"***** EVAL ON DEV SET *****\")\n tf.logging.info(\"***** [DEV EVAL] ***** : precision: {:.4f}, recall: {:.4f}, f1: {:.4f}\".format(eval_dev_result[\"precision\"], eval_dev_result[\"recall\"], dev_f1))\n if dev_f1 > best_dev_f1:\n best_dev_f1, best_dev_prec, best_dev_rec = dev_f1, eval_dev_result[\"precision\"], eval_dev_result[\"recall\"]\n best_ckpt_path = checkpoint_path\n eval_test_result = estimator.evaluate(input_fn=file_based_input_fn_builder(FLAGS.test_file, \n num_window=FLAGS.num_window, window_size=FLAGS.window_size, max_num_mention=FLAGS.max_num_mention, \n is_training=False, drop_remainder=False),steps=698, checkpoint_path=checkpoint_path)\n test_f1 = 2*eval_test_result[\"precision\"] * eval_test_result[\"recall\"] / (eval_test_result[\"precision\"] + eval_test_result[\"recall\"]+1e-10)\n test_f1_when_dev_best, test_prec_when_dev_best, test_rec_when_dev_best = test_f1, eval_test_result[\"precision\"], eval_test_result[\"recall\"]\n tf.logging.info(\"***** EVAL ON TEST SET *****\")\n tf.logging.info(\"***** [TEST EVAL] ***** : precision: {:.4f}, recall: {:.4f}, f1: {:.4f}\".format(eval_test_result[\"precision\"], eval_test_result[\"recall\"], test_f1))\n tf.logging.info(\"*\"*20)\n tf.logging.info(\"- @@@@@ the path to the BEST DEV result is : {}\".format(best_ckpt_path))\n tf.logging.info(\"- @@@@@ BEST DEV F1 : {:.4f}, Precision : {:.4f}, Recall : {:.4f},\".format(best_dev_f1, best_dev_prec, best_dev_rec))\n tf.logging.info(\"- @@@@@ TEST when DEV best F1 : {:.4f}, Precision : {:.4f}, Recall : {:.4f},\".format(test_f1_when_dev_best, test_prec_when_dev_best, test_rec_when_dev_best))\n tf.logging.info(\"- @@@@@ mention_proposal_only_concate {}\".format(FLAGS.mention_proposal_only_concate))\n\n\n if FLAGS.do_predict:\n tp, fp, fn = 0, 0, 0\n epsilon = 1e-10\n for doc_output in estimator.predict(file_based_input_fn_builder(FLAGS.test_file,\n num_window=FLAGS.num_window, window_size=FLAGS.window_size, max_num_mention=FLAGS.max_num_mention,\n is_training=False, drop_remainder=False), checkpoint_path=FLAGS.eval_checkpoint, yield_single_examples=False): \n # iterate over each doc for evaluation\n pred_span_label, gold_span_label = mention_proposal_prediction(FLAGS, doc_output)\n\n tem_tp = np.logical_and(pred_span_label, gold_span_label).sum()\n tem_fp = np.logical_and(pred_span_label, np.logical_not(gold_span_label)).sum()\n tem_fn = np.logical_and(np.logical_not(pred_span_label), gold_span_label).sum()\n\n tp += tem_tp\n fp += tem_fp\n fn += tem_fn\n\n p = tp / (tp+fp+epsilon)\n r = tp / (tp+fn+epsilon)\n f = 2*p*r/(p+r+epsilon)\n tf.logging.info(\"Average precision: {:.4f}, Average recall: {:.4f}, Average F1 {:.4f}\".format(p, r, f))\n\n\n\nif __name__ == '__main__':\n # set the random seed. \n random.seed(FLAGS.seed)\n np.random.seed(FLAGS.seed)\n tf.set_random_seed(FLAGS.seed)\n # start train/evaluate the model.\n tf.app.run()\n\n\n\n\n\n\n"
},
{
"path": "run/run_squad.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Run BERT on SQuAD 1.1 and SQuAD 2.0.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport json\nimport math\nimport os\nimport random\nimport six\nimport tensorflow as tf\n\nfrom bert import modeling\nfrom bert import optimization\nfrom bert import tokenization\n\nflags = tf.flags\n\nFLAGS = flags.FLAGS\n\n## Required parameters\nflags.DEFINE_string(\n \"bert_config_file\", None,\n \"The config json file corresponding to the pre-trained BERT model. \"\n \"This specifies the model architecture.\")\n\nflags.DEFINE_string(\"vocab_file\", None,\n \"The vocabulary file that the BERT model was trained on.\")\n\nflags.DEFINE_string(\n \"output_dir\", None,\n \"The output directory where the model checkpoints will be written.\")\n\n## Other parameters\nflags.DEFINE_string(\"train_file\", None,\n \"SQuAD json for training. E.g., train-v1.1.json\")\n\nflags.DEFINE_string(\n \"predict_file\", None,\n \"SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json\")\n\nflags.DEFINE_string(\n \"init_checkpoint\", None,\n \"Initial checkpoint (usually from a pre-trained BERT model).\")\n\nflags.DEFINE_bool(\n \"do_lower_case\", True,\n \"Whether to lower case the input text. Should be True for uncased \"\n \"models and False for cased models.\")\n\nflags.DEFINE_integer(\n \"max_seq_length\", 384,\n \"The maximum total input sequence length after WordPiece tokenization. \"\n \"Sequences longer than this will be truncated, and sequences shorter \"\n \"than this will be padded.\")\n\nflags.DEFINE_integer(\n \"doc_stride\", 128,\n \"When splitting up a long document into chunks, how much stride to \"\n \"take between chunks.\")\n\nflags.DEFINE_integer(\n \"max_query_length\", 64,\n \"The maximum number of tokens for the question. Questions longer than \"\n \"this will be truncated to this length.\")\n\nflags.DEFINE_bool(\"do_train\", False, \"Whether to run training.\")\n\nflags.DEFINE_bool(\"do_predict\", False, \"Whether to run eval on the dev set.\")\n\nflags.DEFINE_integer(\"train_batch_size\", 32, \"Total batch size for training.\")\n\nflags.DEFINE_integer(\"predict_batch_size\", 8,\n \"Total batch size for predictions.\")\n\nflags.DEFINE_float(\"learning_rate\", 5e-5, \"The initial learning rate for Adam.\")\n\nflags.DEFINE_float(\"num_train_epochs\", 3.0,\n \"Total number of training epochs to perform.\")\n\nflags.DEFINE_float(\n \"warmup_proportion\", 0.1,\n \"Proportion of training to perform linear learning rate warmup for. \"\n \"E.g., 0.1 = 10% of training.\")\n\nflags.DEFINE_integer(\"save_checkpoints_steps\", 1000,\n \"How often to save the model checkpoint.\")\n\nflags.DEFINE_integer(\"iterations_per_loop\", 1000,\n \"How many steps to make in each estimator call.\")\n\nflags.DEFINE_integer(\n \"n_best_size\", 20,\n \"The total number of n-best predictions to generate in the \"\n \"nbest_predictions.json output file.\")\n\nflags.DEFINE_integer(\n \"max_answer_length\", 30,\n \"The maximum length of an answer that can be generated. This is needed \"\n \"because the start and end predictions are not conditioned on one another.\")\n\nflags.DEFINE_bool(\"use_tpu\", False, \"Whether to use TPU or GPU/CPU.\")\n\ntf.flags.DEFINE_string(\n \"tpu_name\", None,\n \"The Cloud TPU to use for training. This should be either the name \"\n \"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 \"\n \"url.\")\n\ntf.flags.DEFINE_string(\n \"tpu_zone\", None,\n \"[Optional] GCE zone where the Cloud TPU is located in. If not \"\n \"specified, we will attempt to automatically detect the GCE project from \"\n \"metadata.\")\n\ntf.flags.DEFINE_string(\n \"gcp_project\", None,\n \"[Optional] Project name for the Cloud TPU-enabled project. If not \"\n \"specified, we will attempt to automatically detect the GCE project from \"\n \"metadata.\")\n\ntf.flags.DEFINE_string(\"master\", None, \"[Optional] TensorFlow master URL.\")\n\nflags.DEFINE_integer(\n \"num_tpu_cores\", 8,\n \"Only used if `use_tpu` is True. Total number of TPU cores to use.\")\n\nflags.DEFINE_bool(\n \"verbose_logging\", False,\n \"If true, all of the warnings related to data processing will be printed. \"\n \"A number of warnings are expected for a normal SQuAD evaluation.\")\n\nflags.DEFINE_bool(\n \"version_2_with_negative\", False,\n \"If true, the SQuAD examples contain some that do not have an answer.\")\n\nflags.DEFINE_float(\n \"null_score_diff_threshold\", 0.0,\n \"If null_score - best_non_null is greater than the threshold predict null.\")\n\n\nclass SquadExample(object):\n \"\"\"A single training/test example for simple sequence classification.\n\n For examples without an answer, the start and end position are -1.\n \"\"\"\n\n def __init__(self,\n qas_id,\n question_text,\n doc_tokens,\n orig_answer_text=None,\n start_position=None,\n end_position=None,\n is_impossible=False):\n self.qas_id = qas_id\n self.question_text = question_text\n self.doc_tokens = doc_tokens\n self.orig_answer_text = orig_answer_text\n self.start_position = start_position\n self.end_position = end_position\n self.is_impossible = is_impossible\n\n def __str__(self):\n return self.__repr__()\n\n def __repr__(self):\n s = \"\"\n s += \"qas_id: %s\" % (tokenization.printable_text(self.qas_id))\n s += \", question_text: %s\" % (\n tokenization.printable_text(self.question_text))\n s += \", doc_tokens: [%s]\" % (\" \".join(self.doc_tokens))\n if self.start_position:\n s += \", start_position: %d\" % (self.start_position)\n if self.start_position:\n s += \", end_position: %d\" % (self.end_position)\n if self.start_position:\n s += \", is_impossible: %r\" % (self.is_impossible)\n return s\n\n\nclass InputFeatures(object):\n \"\"\"A single set of features of data.\"\"\"\n\n def __init__(self,\n unique_id,\n example_index,\n doc_span_index,\n tokens,\n token_to_orig_map,\n token_is_max_context,\n input_ids,\n input_mask,\n segment_ids,\n start_position=None,\n end_position=None,\n is_impossible=None):\n self.unique_id = unique_id\n self.example_index = example_index\n self.doc_span_index = doc_span_index\n self.tokens = tokens\n self.token_to_orig_map = token_to_orig_map\n self.token_is_max_context = token_is_max_context\n self.input_ids = input_ids\n self.input_mask = input_mask\n self.segment_ids = segment_ids\n self.start_position = start_position\n self.end_position = end_position\n self.is_impossible = is_impossible\n\n\ndef read_squad_examples(input_file, is_training):\n \"\"\"Read a SQuAD json file into a list of SquadExample.\"\"\"\n with tf.gfile.Open(input_file, \"r\") as reader:\n input_data = json.load(reader)[\"data\"]\n\n def is_whitespace(c):\n if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n return True\n return False\n\n examples = []\n for entry in input_data:\n for paragraph in entry[\"paragraphs\"]:\n paragraph_text = paragraph[\"context\"]\n doc_tokens = []\n char_to_word_offset = []\n prev_is_whitespace = True\n for c in paragraph_text:\n if is_whitespace(c):\n prev_is_whitespace = True\n else:\n if prev_is_whitespace:\n doc_tokens.append(c)\n else:\n doc_tokens[-1] += c\n prev_is_whitespace = False\n char_to_word_offset.append(len(doc_tokens) - 1)\n\n for qa in paragraph[\"qas\"]:\n qas_id = qa[\"id\"]\n question_text = qa[\"question\"]\n start_position = None\n end_position = None\n orig_answer_text = None\n is_impossible = False\n if is_training:\n\n if FLAGS.version_2_with_negative:\n is_impossible = qa[\"is_impossible\"]\n if (len(qa[\"answers\"]) != 1) and (not is_impossible):\n raise ValueError(\n \"For training, each question should have exactly 1 answer.\")\n if not is_impossible:\n answer = qa[\"answers\"][0]\n orig_answer_text = answer[\"text\"]\n answer_offset = answer[\"answer_start\"]\n answer_length = len(orig_answer_text)\n start_position = char_to_word_offset[answer_offset]\n end_position = char_to_word_offset[answer_offset + answer_length -\n 1]\n # Only add answers where the text can be exactly recovered from the\n # document. If this CAN'T happen it's likely due to weird Unicode\n # stuff so we will just skip the example.\n #\n # Note that this means for training mode, every example is NOT\n # guaranteed to be preserved.\n actual_text = \" \".join(\n doc_tokens[start_position:(end_position + 1)])\n cleaned_answer_text = \" \".join(\n tokenization.whitespace_tokenize(orig_answer_text))\n if actual_text.find(cleaned_answer_text) == -1:\n tf.logging.warning(\"Could not find answer: '%s' vs. '%s'\",\n actual_text, cleaned_answer_text)\n continue\n else:\n start_position = -1\n end_position = -1\n orig_answer_text = \"\"\n\n example = SquadExample(\n qas_id=qas_id,\n question_text=question_text,\n doc_tokens=doc_tokens,\n orig_answer_text=orig_answer_text,\n start_position=start_position,\n end_position=end_position,\n is_impossible=is_impossible)\n examples.append(example)\n\n return examples\n\n\ndef convert_examples_to_features(examples, tokenizer, max_seq_length,\n doc_stride, max_query_length, is_training,\n output_fn):\n \"\"\"Loads a data file into a list of `InputBatch`s.\"\"\"\n\n unique_id = 1000000000\n\n for (example_index, example) in enumerate(examples):\n query_tokens = tokenizer.tokenize(example.question_text)\n\n if len(query_tokens) > max_query_length:\n query_tokens = query_tokens[0:max_query_length]\n\n tok_to_orig_index = []\n orig_to_tok_index = []\n all_doc_tokens = []\n for (i, token) in enumerate(example.doc_tokens):\n orig_to_tok_index.append(len(all_doc_tokens))\n sub_tokens = tokenizer.tokenize(token)\n for sub_token in sub_tokens:\n tok_to_orig_index.append(i)\n all_doc_tokens.append(sub_token)\n\n tok_start_position = None\n tok_end_position = None\n if is_training and example.is_impossible:\n tok_start_position = -1\n tok_end_position = -1\n if is_training and not example.is_impossible:\n tok_start_position = orig_to_tok_index[example.start_position]\n if example.end_position < len(example.doc_tokens) - 1:\n tok_end_position = orig_to_tok_index[example.end_position + 1] - 1\n else:\n tok_end_position = len(all_doc_tokens) - 1\n (tok_start_position, tok_end_position) = _improve_answer_span(\n all_doc_tokens, tok_start_position, tok_end_position, tokenizer,\n example.orig_answer_text)\n\n # The -3 accounts for [CLS], [SEP] and [SEP]\n max_tokens_for_doc = max_seq_length - len(query_tokens) - 3\n\n # We can have documents that are longer than the maximum sequence length.\n # To deal with this we do a sliding window approach, where we take chunks\n # of the up to our max length with a stride of `doc_stride`.\n _DocSpan = collections.namedtuple( # pylint: disable=invalid-name\n \"DocSpan\", [\"start\", \"length\"])\n doc_spans = []\n start_offset = 0\n while start_offset < len(all_doc_tokens):\n length = len(all_doc_tokens) - start_offset\n if length > max_tokens_for_doc:\n length = max_tokens_for_doc\n doc_spans.append(_DocSpan(start=start_offset, length=length))\n if start_offset + length == len(all_doc_tokens):\n break\n start_offset += min(length, doc_stride)\n\n for (doc_span_index, doc_span) in enumerate(doc_spans):\n tokens = []\n token_to_orig_map = {}\n token_is_max_context = {}\n segment_ids = []\n tokens.append(\"[CLS]\")\n segment_ids.append(0)\n for token in query_tokens:\n tokens.append(token)\n segment_ids.append(0)\n tokens.append(\"[SEP]\")\n segment_ids.append(0)\n\n for i in range(doc_span.length):\n split_token_index = doc_span.start + i\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\n\n is_max_context = _check_is_max_context(doc_spans, doc_span_index,\n split_token_index)\n token_is_max_context[len(tokens)] = is_max_context\n tokens.append(all_doc_tokens[split_token_index])\n segment_ids.append(1)\n tokens.append(\"[SEP]\")\n segment_ids.append(1)\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n while len(input_ids) < max_seq_length:\n input_ids.append(0)\n input_mask.append(0)\n segment_ids.append(0)\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n\n start_position = None\n end_position = None\n if is_training and not example.is_impossible:\n # For training, if our document chunk does not contain an annotation\n # we throw it out, since there is nothing to predict.\n doc_start = doc_span.start\n doc_end = doc_span.start + doc_span.length - 1\n out_of_span = False\n if not (tok_start_position >= doc_start and\n tok_end_position <= doc_end):\n out_of_span = True\n if out_of_span:\n start_position = 0\n end_position = 0\n else:\n doc_offset = len(query_tokens) + 2\n start_position = tok_start_position - doc_start + doc_offset\n end_position = tok_end_position - doc_start + doc_offset\n\n if is_training and example.is_impossible:\n start_position = 0\n end_position = 0\n\n if example_index < 20:\n tf.logging.info(\"*** Example ***\")\n tf.logging.info(\"unique_id: %s\" % (unique_id))\n tf.logging.info(\"example_index: %s\" % (example_index))\n tf.logging.info(\"doc_span_index: %s\" % (doc_span_index))\n tf.logging.info(\"tokens: %s\" % \" \".join(\n [tokenization.printable_text(x) for x in tokens]))\n tf.logging.info(\"token_to_orig_map: %s\" % \" \".join(\n [\"%d:%d\" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))\n tf.logging.info(\"token_is_max_context: %s\" % \" \".join([\n \"%d:%s\" % (x, y) for (x, y) in six.iteritems(token_is_max_context)\n ]))\n tf.logging.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n tf.logging.info(\n \"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n tf.logging.info(\n \"segment_ids: %s\" % \" \".join([str(x) for x in segment_ids]))\n if is_training and example.is_impossible:\n tf.logging.info(\"impossible example\")\n if is_training and not example.is_impossible:\n answer_text = \" \".join(tokens[start_position:(end_position + 1)])\n tf.logging.info(\"start_position: %d\" % (start_position))\n tf.logging.info(\"end_position: %d\" % (end_position))\n tf.logging.info(\n \"answer: %s\" % (tokenization.printable_text(answer_text)))\n\n feature = InputFeatures(\n unique_id=unique_id,\n example_index=example_index,\n doc_span_index=doc_span_index,\n tokens=tokens,\n token_to_orig_map=token_to_orig_map,\n token_is_max_context=token_is_max_context,\n input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n start_position=start_position,\n end_position=end_position,\n is_impossible=example.is_impossible)\n\n # Run callback\n output_fn(feature)\n\n unique_id += 1\n\n\ndef _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\n orig_answer_text):\n \"\"\"Returns tokenized answer spans that better match the annotated answer.\"\"\"\n\n # The SQuAD annotations are character based. We first project them to\n # whitespace-tokenized words. But then after WordPiece tokenization, we can\n # often find a \"better match\". For example:\n #\n # Question: What year was John Smith born?\n # Context: The leader was John Smith (1895-1943).\n # Answer: 1895\n #\n # The original whitespace-tokenized answer will be \"(1895-1943).\". However\n # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\n # the exact answer, 1895.\n #\n # However, this is not always possible. Consider the following:\n #\n # Question: What country is the top exporter of electornics?\n # Context: The Japanese electronics industry is the lagest in the world.\n # Answer: Japan\n #\n # In this case, the annotator chose \"Japan\" as a character sub-span of\n # the word \"Japanese\". Since our WordPiece tokenizer does not split\n # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare\n # in SQuAD, but does happen.\n tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n\n for new_start in range(input_start, input_end + 1):\n for new_end in range(input_end, new_start - 1, -1):\n text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n if text_span == tok_answer_text:\n return (new_start, new_end)\n\n return (input_start, input_end)\n\n\ndef _check_is_max_context(doc_spans, cur_span_index, position):\n \"\"\"Check if this is the 'max context' doc span for the token.\"\"\"\n\n # Because of the sliding window approach taken to scoring documents, a single\n # token can appear in multiple documents. E.g.\n # Doc: the man went to the store and bought a gallon of milk\n # Span A: the man went to the\n # Span B: to the store and bought\n # Span C: and bought a gallon of\n # ...\n #\n # Now the word 'bought' will have two scores from spans B and C. We only\n # want to consider the score with \"maximum context\", which we define as\n # the *minimum* of its left and right context (the *sum* of left and\n # right context will always be the same, of course).\n #\n # In the example the maximum context for 'bought' would be span C since\n # it has 1 left context and 3 right context, while span B has 4 left context\n # and 0 right context.\n best_score = None\n best_span_index = None\n for (span_index, doc_span) in enumerate(doc_spans):\n end = doc_span.start + doc_span.length - 1\n if position < doc_span.start:\n continue\n if position > end:\n continue\n num_left_context = position - doc_span.start\n num_right_context = end - position\n score = min(num_left_context, num_right_context) + 0.01 * doc_span.length\n if best_score is None or score > best_score:\n best_score = score\n best_span_index = span_index\n\n return cur_span_index == best_span_index\n\n\ndef create_model(bert_config, is_training, input_ids, input_mask, segment_ids,\n use_one_hot_embeddings):\n \"\"\"Creates a classification model.\"\"\"\n model = modeling.BertModel(\n config=bert_config,\n is_training=is_training,\n input_ids=input_ids,\n input_mask=input_mask,\n token_type_ids=segment_ids,\n use_one_hot_embeddings=use_one_hot_embeddings)\n\n final_hidden = model.get_sequence_output()\n\n final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)\n batch_size = final_hidden_shape[0]\n seq_length = final_hidden_shape[1]\n hidden_size = final_hidden_shape[2]\n\n output_weights = tf.get_variable(\n \"cls/squad/output_weights\", [2, hidden_size],\n initializer=tf.truncated_normal_initializer(stddev=0.02))\n\n output_bias = tf.get_variable(\n \"cls/squad/output_bias\", [2], initializer=tf.zeros_initializer())\n\n final_hidden_matrix = tf.reshape(final_hidden,\n [batch_size * seq_length, hidden_size])\n logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)\n logits = tf.nn.bias_add(logits, output_bias)\n\n logits = tf.reshape(logits, [batch_size, seq_length, 2])\n logits = tf.transpose(logits, [2, 0, 1])\n\n unstacked_logits = tf.unstack(logits, axis=0)\n\n (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])\n\n return (start_logits, end_logits)\n\n\ndef model_fn_builder(bert_config, init_checkpoint, learning_rate,\n num_train_steps, num_warmup_steps, use_tpu,\n use_one_hot_embeddings):\n \"\"\"Returns `model_fn` closure for TPUEstimator.\"\"\"\n\n def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n\n tf.logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n unique_ids = features[\"unique_ids\"]\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n (start_logits, end_logits) = create_model(\n bert_config=bert_config,\n is_training=is_training,\n input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n use_one_hot_embeddings=use_one_hot_embeddings)\n\n tvars = tf.trainable_variables()\n\n initialized_variable_names = {}\n scaffold_fn = None\n if init_checkpoint:\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n if use_tpu:\n\n def tpu_scaffold():\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n return tf.train.Scaffold()\n\n scaffold_fn = tpu_scaffold\n else:\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = None\n if mode == tf.estimator.ModeKeys.TRAIN:\n seq_length = modeling.get_shape_list(input_ids)[1]\n\n def compute_loss(logits, positions):\n one_hot_positions = tf.one_hot(\n positions, depth=seq_length, dtype=tf.float32)\n log_probs = tf.nn.log_softmax(logits, axis=-1)\n loss = -tf.reduce_mean(\n tf.reduce_sum(one_hot_positions * log_probs, axis=-1))\n return loss\n\n start_positions = features[\"start_positions\"]\n end_positions = features[\"end_positions\"]\n\n start_loss = compute_loss(start_logits, start_positions)\n end_loss = compute_loss(end_logits, end_positions)\n\n total_loss = (start_loss + end_loss) / 2.0\n\n train_op = optimization.create_optimizer(\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)\n\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn)\n elif mode == tf.estimator.ModeKeys.PREDICT:\n predictions = {\n \"unique_ids\": unique_ids,\n \"start_logits\": start_logits,\n \"end_logits\": end_logits,\n }\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)\n else:\n raise ValueError(\n \"Only TRAIN and PREDICT modes are supported: %s\" % (mode))\n\n return output_spec\n\n return model_fn\n\n\ndef input_fn_builder(input_file, seq_length, is_training, drop_remainder):\n \"\"\"Creates an `input_fn` closure to be passed to TPUEstimator.\"\"\"\n\n name_to_features = {\n \"unique_ids\": tf.FixedLenFeature([], tf.int64),\n \"input_ids\": tf.FixedLenFeature([seq_length], tf.int64),\n \"input_mask\": tf.FixedLenFeature([seq_length], tf.int64),\n \"segment_ids\": tf.FixedLenFeature([seq_length], tf.int64),\n }\n\n if is_training:\n name_to_features[\"start_positions\"] = tf.FixedLenFeature([], tf.int64)\n name_to_features[\"end_positions\"] = tf.FixedLenFeature([], tf.int64)\n\n def _decode_record(record, name_to_features):\n \"\"\"Decodes a record to a TensorFlow example.\"\"\"\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example\n\n def input_fn(params):\n \"\"\"The actual input function.\"\"\"\n batch_size = params[\"batch_size\"]\n\n # For training, we want a lot of parallel reading and shuffling.\n # For eval, we want no shuffling and parallel reading doesn't matter.\n d = tf.data.TFRecordDataset(input_file)\n if is_training:\n d = d.repeat()\n d = d.shuffle(buffer_size=100)\n\n d = d.apply(\n tf.contrib.data.map_and_batch(\n lambda record: _decode_record(record, name_to_features),\n batch_size=batch_size,\n drop_remainder=drop_remainder))\n\n return d\n\n return input_fn\n\n\nRawResult = collections.namedtuple(\"RawResult\",\n [\"unique_id\", \"start_logits\", \"end_logits\"])\n\n\ndef write_predictions(all_examples, all_features, all_results, n_best_size,\n max_answer_length, do_lower_case, output_prediction_file,\n output_nbest_file, output_null_log_odds_file):\n \"\"\"Write final predictions to the json file and log-odds of null if needed.\"\"\"\n tf.logging.info(\"Writing predictions to: %s\" % (output_prediction_file))\n tf.logging.info(\"Writing nbest to: %s\" % (output_nbest_file))\n\n example_index_to_features = collections.defaultdict(list)\n for feature in all_features:\n example_index_to_features[feature.example_index].append(feature)\n\n unique_id_to_result = {}\n for result in all_results:\n unique_id_to_result[result.unique_id] = result\n\n _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name\n \"PrelimPrediction\",\n [\"feature_index\", \"start_index\", \"end_index\", \"start_logit\", \"end_logit\"])\n\n all_predictions = collections.OrderedDict()\n all_nbest_json = collections.OrderedDict()\n scores_diff_json = collections.OrderedDict()\n\n for (example_index, example) in enumerate(all_examples):\n features = example_index_to_features[example_index]\n\n prelim_predictions = []\n # keep track of the minimum score of null start+end of position 0\n score_null = 1000000 # large and positive\n min_null_feature_index = 0 # the paragraph slice with min mull score\n null_start_logit = 0 # the start logit at the slice with min null score\n null_end_logit = 0 # the end logit at the slice with min null score\n for (feature_index, feature) in enumerate(features):\n result = unique_id_to_result[feature.unique_id]\n start_indexes = _get_best_indexes(result.start_logits, n_best_size)\n end_indexes = _get_best_indexes(result.end_logits, n_best_size)\n # if we could have irrelevant answers, get the min score of irrelevant\n if FLAGS.version_2_with_negative:\n feature_null_score = result.start_logits[0] + result.end_logits[0]\n if feature_null_score < score_null:\n score_null = feature_null_score\n min_null_feature_index = feature_index\n null_start_logit = result.start_logits[0]\n null_end_logit = result.end_logits[0]\n for start_index in start_indexes:\n for end_index in end_indexes:\n # We could hypothetically create invalid predictions, e.g., predict\n # that the start of the span is in the question. We throw out all\n # invalid predictions.\n if start_index >= len(feature.tokens):\n continue\n if end_index >= len(feature.tokens):\n continue\n if start_index not in feature.token_to_orig_map:\n continue\n if end_index not in feature.token_to_orig_map:\n continue\n if not feature.token_is_max_context.get(start_index, False):\n continue\n if end_index < start_index:\n continue\n length = end_index - start_index + 1\n if length > max_answer_length:\n continue\n prelim_predictions.append(\n _PrelimPrediction(\n feature_index=feature_index,\n start_index=start_index,\n end_index=end_index,\n start_logit=result.start_logits[start_index],\n end_logit=result.end_logits[end_index]))\n\n if FLAGS.version_2_with_negative:\n prelim_predictions.append(\n _PrelimPrediction(\n feature_index=min_null_feature_index,\n start_index=0,\n end_index=0,\n start_logit=null_start_logit,\n end_logit=null_end_logit))\n prelim_predictions = sorted(\n prelim_predictions,\n key=lambda x: (x.start_logit + x.end_logit),\n reverse=True)\n\n _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name\n \"NbestPrediction\", [\"text\", \"start_logit\", \"end_logit\"])\n\n seen_predictions = {}\n nbest = []\n for pred in prelim_predictions:\n if len(nbest) >= n_best_size:\n break\n feature = features[pred.feature_index]\n if pred.start_index > 0: # this is a non-null prediction\n tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]\n orig_doc_start = feature.token_to_orig_map[pred.start_index]\n orig_doc_end = feature.token_to_orig_map[pred.end_index]\n orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]\n tok_text = \" \".join(tok_tokens)\n\n # De-tokenize WordPieces that have been split off.\n tok_text = tok_text.replace(\" ##\", \"\")\n tok_text = tok_text.replace(\"##\", \"\")\n\n # Clean whitespace\n tok_text = tok_text.strip()\n tok_text = \" \".join(tok_text.split())\n orig_text = \" \".join(orig_tokens)\n\n final_text = get_final_text(tok_text, orig_text, do_lower_case)\n if final_text in seen_predictions:\n continue\n\n seen_predictions[final_text] = True\n else:\n final_text = \"\"\n seen_predictions[final_text] = True\n\n nbest.append(\n _NbestPrediction(\n text=final_text,\n start_logit=pred.start_logit,\n end_logit=pred.end_logit))\n\n # if we didn't inlude the empty option in the n-best, inlcude it\n if FLAGS.version_2_with_negative:\n if \"\" not in seen_predictions:\n nbest.append(\n _NbestPrediction(\n text=\"\", start_logit=null_start_logit,\n end_logit=null_end_logit))\n # In very rare edge cases we could have no valid predictions. So we\n # just create a nonce prediction in this case to avoid failure.\n if not nbest:\n nbest.append(\n _NbestPrediction(text=\"empty\", start_logit=0.0, end_logit=0.0))\n\n assert len(nbest) >= 1\n\n total_scores = []\n best_non_null_entry = None\n for entry in nbest:\n total_scores.append(entry.start_logit + entry.end_logit)\n if not best_non_null_entry:\n if entry.text:\n best_non_null_entry = entry\n\n probs = _compute_softmax(total_scores)\n\n nbest_json = []\n for (i, entry) in enumerate(nbest):\n output = collections.OrderedDict()\n output[\"text\"] = entry.text\n output[\"probability\"] = probs[i]\n output[\"start_logit\"] = entry.start_logit\n output[\"end_logit\"] = entry.end_logit\n nbest_json.append(output)\n\n assert len(nbest_json) >= 1\n\n if not FLAGS.version_2_with_negative:\n all_predictions[example.qas_id] = nbest_json[0][\"text\"]\n else:\n # predict \"\" iff the null score - the score of best non-null > threshold\n score_diff = score_null - best_non_null_entry.start_logit - (\n best_non_null_entry.end_logit)\n scores_diff_json[example.qas_id] = score_diff\n if score_diff > FLAGS.null_score_diff_threshold:\n all_predictions[example.qas_id] = \"\"\n else:\n all_predictions[example.qas_id] = best_non_null_entry.text\n\n all_nbest_json[example.qas_id] = nbest_json\n\n with tf.gfile.GFile(output_prediction_file, \"w\") as writer:\n writer.write(json.dumps(all_predictions, indent=4) + \"\\n\")\n\n with tf.gfile.GFile(output_nbest_file, \"w\") as writer:\n writer.write(json.dumps(all_nbest_json, indent=4) + \"\\n\")\n\n if FLAGS.version_2_with_negative:\n with tf.gfile.GFile(output_null_log_odds_file, \"w\") as writer:\n writer.write(json.dumps(scores_diff_json, indent=4) + \"\\n\")\n\n\ndef get_final_text(pred_text, orig_text, do_lower_case):\n \"\"\"Project the tokenized prediction back to the original text.\"\"\"\n\n # When we created the data, we kept track of the alignment between original\n # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So\n # now `orig_text` contains the span of our original text corresponding to the\n # span that we predicted.\n #\n # However, `orig_text` may contain extra characters that we don't want in\n # our prediction.\n #\n # For example, let's say:\n # pred_text = steve smith\n # orig_text = Steve Smith's\n #\n # We don't want to return `orig_text` because it contains the extra \"'s\".\n #\n # We don't want to return `pred_text` because it's already been normalized\n # (the SQuAD eval script also does punctuation stripping/lower casing but\n # our tokenizer does additional normalization like stripping accent\n # characters).\n #\n # What we really want to return is \"Steve Smith\".\n #\n # Therefore, we have to apply a semi-complicated alignment heruistic between\n # `pred_text` and `orig_text` to get a character-to-charcter alignment. This\n # can fail in certain cases in which case we just return `orig_text`.\n\n def _strip_spaces(text):\n ns_chars = []\n ns_to_s_map = collections.OrderedDict()\n for (i, c) in enumerate(text):\n if c == \" \":\n continue\n ns_to_s_map[len(ns_chars)] = i\n ns_chars.append(c)\n ns_text = \"\".join(ns_chars)\n return (ns_text, ns_to_s_map)\n\n # We first tokenize `orig_text`, strip whitespace from the result\n # and `pred_text`, and check if they are the same length. If they are\n # NOT the same length, the heuristic has failed. If they are the same\n # length, we assume the characters are one-to-one aligned.\n tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)\n\n tok_text = \" \".join(tokenizer.tokenize(orig_text))\n\n start_position = tok_text.find(pred_text)\n if start_position == -1:\n if FLAGS.verbose_logging:\n tf.logging.info(\n \"Unable to find text: '%s' in '%s'\" % (pred_text, orig_text))\n return orig_text\n end_position = start_position + len(pred_text) - 1\n\n (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\n (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\n\n if len(orig_ns_text) != len(tok_ns_text):\n if FLAGS.verbose_logging:\n tf.logging.info(\"Length not equal after stripping spaces: '%s' vs '%s'\",\n orig_ns_text, tok_ns_text)\n return orig_text\n\n # We then project the characters in `pred_text` back to `orig_text` using\n # the character-to-character alignment.\n tok_s_to_ns_map = {}\n for (i, tok_index) in six.iteritems(tok_ns_to_s_map):\n tok_s_to_ns_map[tok_index] = i\n\n orig_start_position = None\n if start_position in tok_s_to_ns_map:\n ns_start_position = tok_s_to_ns_map[start_position]\n if ns_start_position in orig_ns_to_s_map:\n orig_start_position = orig_ns_to_s_map[ns_start_position]\n\n if orig_start_position is None:\n if FLAGS.verbose_logging:\n tf.logging.info(\"Couldn't map start position\")\n return orig_text\n\n orig_end_position = None\n if end_position in tok_s_to_ns_map:\n ns_end_position = tok_s_to_ns_map[end_position]\n if ns_end_position in orig_ns_to_s_map:\n orig_end_position = orig_ns_to_s_map[ns_end_position]\n\n if orig_end_position is None:\n if FLAGS.verbose_logging:\n tf.logging.info(\"Couldn't map end position\")\n return orig_text\n\n output_text = orig_text[orig_start_position:(orig_end_position + 1)]\n return output_text\n\n\ndef _get_best_indexes(logits, n_best_size):\n \"\"\"Get the n-best logits from a list.\"\"\"\n index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)\n\n best_indexes = []\n for i in range(len(index_and_score)):\n if i >= n_best_size:\n break\n best_indexes.append(index_and_score[i][0])\n return best_indexes\n\n\ndef _compute_softmax(scores):\n \"\"\"Compute softmax probability over raw logits.\"\"\"\n if not scores:\n return []\n\n max_score = None\n for score in scores:\n if max_score is None or score > max_score:\n max_score = score\n\n exp_scores = []\n total_sum = 0.0\n for score in scores:\n x = math.exp(score - max_score)\n exp_scores.append(x)\n total_sum += x\n\n probs = []\n for score in exp_scores:\n probs.append(score / total_sum)\n return probs\n\n\nclass FeatureWriter(object):\n \"\"\"Writes InputFeature to TF example file.\"\"\"\n\n def __init__(self, filename, is_training):\n self.filename = filename\n self.is_training = is_training\n self.num_features = 0\n self._writer = tf.python_io.TFRecordWriter(filename)\n\n def process_feature(self, feature):\n \"\"\"Write a InputFeature to the TFRecordWriter as a tf.train.Example.\"\"\"\n self.num_features += 1\n\n def create_int_feature(values):\n feature = tf.train.Feature(\n int64_list=tf.train.Int64List(value=list(values)))\n return feature\n\n features = collections.OrderedDict()\n features[\"unique_ids\"] = create_int_feature([feature.unique_id])\n features[\"input_ids\"] = create_int_feature(feature.input_ids)\n features[\"input_mask\"] = create_int_feature(feature.input_mask)\n features[\"segment_ids\"] = create_int_feature(feature.segment_ids)\n\n if self.is_training:\n features[\"start_positions\"] = create_int_feature([feature.start_position])\n features[\"end_positions\"] = create_int_feature([feature.end_position])\n impossible = 0\n if feature.is_impossible:\n impossible = 1\n features[\"is_impossible\"] = create_int_feature([impossible])\n\n tf_example = tf.train.Example(features=tf.train.Features(feature=features))\n self._writer.write(tf_example.SerializeToString())\n\n def close(self):\n self._writer.close()\n\n\ndef validate_flags_or_throw(bert_config):\n \"\"\"Validate the input FLAGS or throw an exception.\"\"\"\n tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,\n FLAGS.init_checkpoint)\n\n if not FLAGS.do_train and not FLAGS.do_predict:\n raise ValueError(\"At least one of `do_train` or `do_predict` must be True.\")\n\n if FLAGS.do_train:\n if not FLAGS.train_file:\n raise ValueError(\n \"If `do_train` is True, then `train_file` must be specified.\")\n if FLAGS.do_predict:\n if not FLAGS.predict_file:\n raise ValueError(\n \"If `do_predict` is True, then `predict_file` must be specified.\")\n\n if FLAGS.max_seq_length > bert_config.max_position_embeddings:\n raise ValueError(\n \"Cannot use sequence length %d because the BERT model \"\n \"was only trained up to sequence length %d\" %\n (FLAGS.max_seq_length, bert_config.max_position_embeddings))\n\n if FLAGS.max_seq_length <= FLAGS.max_query_length + 3:\n raise ValueError(\n \"The max_seq_length (%d) must be greater than max_query_length \"\n \"(%d) + 3\" % (FLAGS.max_seq_length, FLAGS.max_query_length))\n\n\ndef main(_):\n tf.logging.set_verbosity(tf.logging.INFO)\n\n bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)\n\n validate_flags_or_throw(bert_config)\n\n tf.gfile.MakeDirs(FLAGS.output_dir)\n\n tokenizer = tokenization.FullTokenizer(\n vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)\n\n tpu_cluster_resolver = None\n if FLAGS.use_tpu and FLAGS.tpu_name:\n tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(\n FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)\n\n is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2\n run_config = tf.contrib.tpu.RunConfig(\n cluster=tpu_cluster_resolver,\n master=FLAGS.master,\n model_dir=FLAGS.output_dir,\n save_checkpoints_steps=FLAGS.save_checkpoints_steps,\n tpu_config=tf.contrib.tpu.TPUConfig(\n iterations_per_loop=FLAGS.iterations_per_loop,\n num_shards=FLAGS.num_tpu_cores,\n per_host_input_for_training=is_per_host))\n\n train_examples = None\n num_train_steps = None\n num_warmup_steps = None\n if FLAGS.do_train:\n train_examples = read_squad_examples(\n input_file=FLAGS.train_file, is_training=True)\n num_train_steps = int(\n len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)\n num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)\n\n # Pre-shuffle the input to avoid having to make a very large shuffle\n # buffer in in the `input_fn`.\n rng = random.Random(12345)\n rng.shuffle(train_examples)\n\n model_fn = model_fn_builder(\n bert_config=bert_config,\n init_checkpoint=FLAGS.init_checkpoint,\n learning_rate=FLAGS.learning_rate,\n num_train_steps=num_train_steps,\n num_warmup_steps=num_warmup_steps,\n use_tpu=FLAGS.use_tpu,\n use_one_hot_embeddings=FLAGS.use_tpu)\n\n # If TPU is not available, this will fall back to normal Estimator on CPU\n # or GPU.\n estimator = tf.contrib.tpu.TPUEstimator(\n use_tpu=FLAGS.use_tpu,\n model_fn=model_fn,\n config=run_config,\n train_batch_size=FLAGS.train_batch_size,\n predict_batch_size=FLAGS.predict_batch_size)\n\n if FLAGS.do_train:\n # We write to a temporary file to avoid storing very large constant tensors\n # in memory.\n train_writer = FeatureWriter(\n filename=os.path.join(FLAGS.output_dir, \"train.tf_record\"),\n is_training=True)\n convert_examples_to_features(\n examples=train_examples,\n tokenizer=tokenizer,\n max_seq_length=FLAGS.max_seq_length,\n doc_stride=FLAGS.doc_stride,\n max_query_length=FLAGS.max_query_length,\n is_training=True,\n output_fn=train_writer.process_feature)\n train_writer.close()\n\n tf.logging.info(\"***** Running training *****\")\n tf.logging.info(\" Num orig examples = %d\", len(train_examples))\n tf.logging.info(\" Num split examples = %d\", train_writer.num_features)\n tf.logging.info(\" Batch size = %d\", FLAGS.train_batch_size)\n tf.logging.info(\" Num steps = %d\", num_train_steps)\n del train_examples\n\n train_input_fn = input_fn_builder(\n input_file=train_writer.filename,\n seq_length=FLAGS.max_seq_length,\n is_training=True,\n drop_remainder=True)\n estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)\n\n if FLAGS.do_predict:\n eval_examples = read_squad_examples(\n input_file=FLAGS.predict_file, is_training=False)\n\n eval_writer = FeatureWriter(\n filename=os.path.join(FLAGS.output_dir, \"eval.tf_record\"),\n is_training=False)\n eval_features = []\n\n def append_feature(feature):\n eval_features.append(feature)\n eval_writer.process_feature(feature)\n\n convert_examples_to_features(\n examples=eval_examples,\n tokenizer=tokenizer,\n max_seq_length=FLAGS.max_seq_length,\n doc_stride=FLAGS.doc_stride,\n max_query_length=FLAGS.max_query_length,\n is_training=False,\n output_fn=append_feature)\n eval_writer.close()\n\n tf.logging.info(\"***** Running predictions *****\")\n tf.logging.info(\" Num orig examples = %d\", len(eval_examples))\n tf.logging.info(\" Num split examples = %d\", len(eval_features))\n tf.logging.info(\" Batch size = %d\", FLAGS.predict_batch_size)\n\n all_results = []\n\n predict_input_fn = input_fn_builder(\n input_file=eval_writer.filename,\n seq_length=FLAGS.max_seq_length,\n is_training=False,\n drop_remainder=False)\n\n # If running eval on the TPU, you will need to specify the number of\n # steps.\n all_results = []\n for result in estimator.predict(\n predict_input_fn, yield_single_examples=True):\n if len(all_results) % 1000 == 0:\n tf.logging.info(\"Processing example: %d\" % (len(all_results)))\n unique_id = int(result[\"unique_ids\"])\n start_logits = [float(x) for x in result[\"start_logits\"].flat]\n end_logits = [float(x) for x in result[\"end_logits\"].flat]\n all_results.append(\n RawResult(\n unique_id=unique_id,\n start_logits=start_logits,\n end_logits=end_logits))\n\n output_prediction_file = os.path.join(FLAGS.output_dir, \"predictions.json\")\n output_nbest_file = os.path.join(FLAGS.output_dir, \"nbest_predictions.json\")\n output_null_log_odds_file = os.path.join(FLAGS.output_dir, \"null_odds.json\")\n\n write_predictions(eval_examples, eval_features, all_results,\n FLAGS.n_best_size, FLAGS.max_answer_length,\n FLAGS.do_lower_case, output_prediction_file,\n output_nbest_file, output_null_log_odds_file)\n\n\nif __name__ == \"__main__\":\n flags.mark_flag_as_required(\"vocab_file\")\n flags.mark_flag_as_required(\"bert_config_file\")\n flags.mark_flag_as_required(\"output_dir\")\n tf.app.run()\n"
},
{
"path": "run/transform_spanbert_pytorch_to_tf.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# transform pytorch .bin models to tensorflow ckpt \n\n\nimport os\nimport sys \nimport shutil \nimport torch\nimport argparse \nimport random \nimport numpy as np \nimport tensorflow as tf \n\nREPO_PATH = \"/\".join(os.path.realpath(__file__).split(\"/\")[:-2])\n\nif REPO_PATH not in sys.path:\n sys.path.insert(0, REPO_PATH)\n\n\nfrom bert import modeling \nfrom utils.load_pytorch_to_tf import load_from_pytorch_checkpoint \n\n\ndef load_models(bert_config_path, ):\n bert_config = modeling.BertConfig.from_json_file(bert_config_path)\n input_ids = tf.ones((8, 128), tf.int32)\n\n model = modeling.BertModel(\n config=bert_config,\n is_training=False, \n input_ids=input_ids,\n use_one_hot_embeddings=False, \n scope=\"bert\")\n\n return model, bert_config \n\n\ndef copy_checkpoint(source, target):\n for ext in (\".index\", \".data-00000-of-00001\"):\n shutil.copyfile(source + ext, target + ext)\n\n\ndef main(bert_config_path, bert_ckpt_path, pytorch_init_checkpoint, output_tf_dir):\n\n with tf.Session() as session:\n model, bert_config = load_models(bert_config_path)\n tvars = tf.trainable_variables()\n assignment_map, initialized_variable_names = modeling.get_assignment_map_from_checkpoint(tvars, bert_ckpt_path)\n session.run(tf.global_variables_initializer())\n init_from_checkpoint = load_from_pytorch_checkpoint\n init_from_checkpoint(pytorch_init_checkpoint, assignment_map)\n\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n print(\"name = %s, shape = %s%s\" % (var.name, var.shape, init_string))\n \n saver = tf.train.Saver()\n saver.save(session, os.path.join(output_tf_dir, \"model\"), global_step=100)\n copy_checkpoint(os.path.join(output_tf_dir, \"model-{}\".format(str(100))), os.path.join(output_tf_dir, \"bert_model.ckpt\"))\n print(\"=*=\"*30)\n print(\"save models : {}\".format(output_tf_dir))\n print(\"=*=\"*30)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--spanbert_config_path\", default=\"/home/lixiaoya/spanbert_base_cased/config.json\", type=str)\n parser.add_argument(\"--bert_tf_ckpt_path\", default=\"/home/lixiaoya/cased_L-12_H-768_A-12/bert_model.ckpt\", type=str)\n parser.add_argument(\"--spanbert_pytorch_bin_path\", default=\"/home/lixiaoya/spanbert_base_cased/pytorch_model.bin\", type=str)\n parser.add_argument(\"--output_spanbert_tf_dir\", default=\"/home/lixiaoya/tf_spanbert_base_case\", type=str)\n parser.add_argument(\"--seed\", default=2333, type=int)\n\n\n args = parser.parse_args()\n\n random.seed(args.seed)\n np.random.seed(args.seed)\n torch.manual_seed(args.seed)\n tf.set_random_seed(args.seed)\n torch.cuda.manual_seed_all(args.seed)\n\n os.makedirs(args.output_spanbert_tf_dir, exist_ok=True)\n\n try:\n shutil(args.spanbert_config_path, args.output_spanbert_tf_dir)\n except:\n print(\"#=#\"*30)\n print(\"copy spanbert_config from {} to {}\".format(args.spanbert_config_path, args.output_spanbert_tf_dir))\n\n return args\n\n\nif __name__ == \"__main__\":\n\n args_config = parse_args()\n\n main(args_config.spanbert_config_path, args_config.bert_tf_ckpt_path, args_config.spanbert_pytorch_bin_path, args_config.output_spanbert_tf_dir)\n\n # \n # Please refer to scripts/data/transform_ckpt_pytorch_to_tf.sh \n # \n\n # for spanbert large \n # \n # python3 transform_spanbert_pytorch_to_tf.py \\\n # --spanbert_config_path /xiaoya/pretrain_ckpt/spanbert_large_cased/config.json \\\n # --bert_tf_ckpt_path /xiaoya/pretrain_ckpt/cased_L-24_H-1024_A-16/bert_model.ckpt \\\n # --spanbert_pytorch_bin_path /xiaoya/pretrain_ckpt/spanbert_large_cased/pytorch_model.bin \\\n # --output_spanbert_tf_dir /xiaoya/pretrain_ckpt/tf_spanbert_large_cased\n\n\n # for spanbert base \n # \n # python3 transform_spanbert_pytorch_to_tf.py \\\n # --spanbert_config_path /xiaoya/pretrain_ckpt/spanbert_base_cased/config.json \\\n # --bert_tf_ckpt_path /xiaoya/pretrain_ckpt/cased_L-12_H-768_A-12/bert_model.ckpt \\\n # --spanbert_pytorch_bin_path /xiaoya/pretrain_ckpt/spanbert_base_cased/pytorch_model.bin \\\n # --output_spanbert_tf_dir /xiaoya/pretrain_ckpt/tf_spanbert_base_cased\n\n"
},
{
"path": "scripts/data/download_pretrained_mlm.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*- \n\n\n\n# Author: xiaoy li \n# description:\n# download pretrained model ckpt \n\n\n\nBERT_PRETRAIN_CKPT=$1\nMODEL_NAME=$2\n\n\nif [[ $MODEL_NAME == \"bert_base\" ]]; then\n mkdir -p $BERT_PRETRAIN_CKPT\n echo \"DownLoad BERT Cased Base\"\n wget https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip -P $BERT_PRETRAIN_CKPT\n unzip $BERT_PRETRAIN_CKPT/cased_L-12_H-768_A-12.zip -d $BERT_PRETRAIN_CKPT\n rm $BERT_PRETRAIN_CKPT/cased_L-12_H-768_A-12.zip\nelif [[ $MODEL_NAME == \"bert_large\" ]]; then\n echo \"DownLoad BERT Cased Large\"\n wget https://storage.googleapis.com/bert_models/2018_10_18/cased_L-24_H-1024_A-16.zip -P $BERT_PRETRAIN_CKPT\n unzip $BERT_PRETRAIN_CKPT/cased_L-24_H-1024_A-16.zip -d $BERT_PRETRAIN_CKPT\n rm $BERT_PRETRAIN_CKPT/cased_L-24_H-1024_A-16.zip\nelif [[ $MODEL_NAME == \"spanbert_base\" ]]; then\n echo \"DownLoad Span-BERT Cased Base\"\n wget https://dl.fbaipublicfiles.com/fairseq/models/spanbert_hf_base.tar.gz -P $BERT_PRETRAIN_CKPT \n tar -zxvf $BERT_PRETRAIN_CKPT/spanbert_hf_base.tar.gz -C $BERT_PRETRAIN_CKPT\n rm $BERT_PRETRAIN_CKPT/spanbert_hf_base.tar.gz\nelif [[ $MODEL_NAME == \"spanbert_large\" ]]; then\n echo \"DownLoad Span-BERT Cased Large\"\n wget https://dl.fbaipublicfiles.com/fairseq/models/spanbert_hf.tar.gz -P $BERT_PRETRAIN_CKPT\n tar -zxvf $BERT_PRETRAIN_CKPT/spanbert_hf.tar.gz -C $BERT_PRETRAIN_CKPT\n rm $BERT_PRETRAIN_CKPT/spanbert_hf.tar.gz\nelif [[ $MODEL_NAME == \"bert_tiny\" ]]; then\n each \"DownLoad BERT Uncased Tiny; Helps to debug on GPU.\"\n wget https://storage.googleapis.com/bert_models/2020_02_20/uncased_L-2_H-128_A-2.zip -P $BERT_PRETRAIN_CKPT \n tar -zxvf $BERT_PRETRAIN_CKPT/uncased_L-2_H-128_A-2.zip -C $BERT_PRETRAIN_CKPT\n rm $BERT_PRETRAIN_CKPT/uncased_L-2_H-128_A-2.zip\nelse\n echo 'Unknown argment 2 (Model Sign)'\nfi "
},
{
"path": "scripts/data/generate_tfrecord_dataset.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*-\n\n\n\n# author: xiaoy li\n# description:\n# generate train/dev/test tfrecord files for training the model. \n# example:\n# bash generate_tfrecord_dataset.sh /path-to-conll-coreference-resolution-dataset /path-to-save-tfrecord-for-training /cased_L-12_H-768_A-12/vocab.txt\n\n\n\nREPO_PATH=/home/lixiaoya/coref-tf\nexport PYTHONPATH=$REPO_PATH\n\nsource_dir=$1\ntarget_dir=$2\nvocab_file=$3\n\nmkdir -p ${target_dir}\n\n\npython3 ${REPO_PATH}/run/build_dataset_to_tfrecord.py \\\n--source_files_dir $source_dir \\\n--target_output_dir $target_dir \\\n--num_window 2 \\\n--window_size 64 \\\n--max_num_mention 50 \\\n--max_num_cluster 40 \\\n--vocab_file $vocab_file \\\n--language english \\\n--max_doc_length 600 "
},
{
"path": "scripts/data/preprocess_ontonotes_annfiles.sh",
"content": "#!/usr/bin/env bash \n\n\n# author: xiaoy li \n# description:\n# generate annotated CONLL-2012 coreference resolution datasets from the official released OntoNotes 5.0 dataset. \n# \n######################################\n# NOTICE:\n######################################\n# the scripts only work with python 2.\n# if you want to run with python 3, please refer to https://github.com/huggingface/neuralcoref/blob/master/neuralcoref/train/training.md#get-the-data\n# Thanks to their amazing job ! \n# \n# Reference: \n# https://github.com/huggingface/neuralcoref/blob/master/neuralcoref/train/training.md#get-the-data\n# https://github.com/mandarjoshi90/coref\n# \n\n\npath_to_ontonotes5.0_directory=$1\npath_to_save_processed_data_directory=$2\nlanguage=$3\n\n\ndlx() {\n wget -P $path_to_save_processed_data_directory $1/$2\n tar -xvzf $path_to_save_processed_data_directory/$2 -C $path_to_save_processed_data_directory\n rm $path_to_save_processed_data_directory/$2\n}\n\n\nconll_url=http://conll.cemantix.org/2012/download\ndlx $conll_url conll-2012-train.v4.tar.gz\ndlx $conll_url conll-2012-development.v4.tar.gz\ndlx $conll_url/test conll-2012-test-key.tar.gz\ndlx $conll_url/test conll-2012-test-official.v9.tar.gz\n\ndlx $conll_url conll-2012-scripts.v3.tar.gz\ndlx http://conll.cemantix.org/download reference-coreference-scorers.v8.01.tar.gz\n\nbash $path_to_save_processed_data_directory/conll-2012/v3/scripts/skeleton2conll.sh -D $path_to_ontonotes5.0_directory/data/files/data $path_to_save_processed_data_directory/conll-2012\n\nfunction compile_partition() {\n rm -f $2.$5.$3$4\n cat $path_to_save_processed_data_directory/conll-2012/$3/data/$1/data/$5/annotations/*/*/*/*.$3$4 >> $path_to_save_processed_data_directory/$2.$5.$3$4\n}\n\nfunction compile_language() {\n compile_partition development dev v4 _gold_conll $1\n compile_partition train train v4 _gold_conll $1\n compile_partition test test v4 _gold_conll $1\n}\n\ncompile_language $language\n\n\n"
},
{
"path": "scripts/data/transform_ckpt_pytorch_to_tf.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*-\n\n\n\n# author: xiaoy li\n# description:\n# transform trained spanbert language model from pytorch(.bin) to tensorflow(.ckpt). \n# PLEASE NOTICE: the same scale(Base/Large) BERT(TF) Models are also necessary. \n\n\n\nREPO_PATH=/home/lixiaoya/coref-tf\nexport PYTHONPATH=${REPO_PATH}\n\n\nMODEL_NAME=$1\nPATH_TO_SPANBERT_PYTORCH_DIR=$2\nPATH_TO_SAME_SCALE_BERT_TF_DIR=$3\nPATH_TO_SAVE_SPANBERT_TF_DIR=$4\n\n\nif [[ $MODEL_NAME == \"spanbert_base\" ]]; then\n # spanbert large \n echo \"Transform SpanBERT Cased Base from Pytorch To TF\"\n python3 ${REPO_PATH}/run/transform_spanbert_pytorch_to_tf.py \\\n --spanbert_config_path $PATH_TO_SPANBERT_PYTORCH_DIR/config.json \\\n --bert_tf_ckpt_path $PATH_TO_SAME_SCALE_BERT_TF_DIR/bert_model.ckpt \\\n --spanbert_pytorch_bin_path $PATH_TO_SPANBERT_PYTORCH_DIR/pytorch_model.bin \\\n --output_spanbert_tf_dir $PATH_TO_SAVE_SPANBERT_TF_DIR\nelif [[ $MODEL_NAME == \"spanbert_large\" ]]; then\n # spanbert base \n echo \"Transform SpanBERT Cased Large from Pytorch To TF\"\n python3 ${REPO_PATH}/run/transform_spanbert_pytorch_to_tf.py \\\n --spanbert_config_path $PATH_TO_SPANBERT_PYTORCH_DIR/config.json \\\n --bert_tf_ckpt_path $PATH_TO_SAME_SCALE_BERT_TF_DIR/bert_model.ckpt \\\n --spanbert_pytorch_bin_path $PATH_TO_SPANBERT_PYTORCH_DIR/pytorch_model.bin \\\n --output_spanbert_tf_dir $PATH_TO_SAVE_SPANBERT_TF_DIR\nelse\n echo 'Unknown argment 1 (Model Sign)'\nfi "
},
{
"path": "scripts/models/corefqa_gpu.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# train and evaluate the middle checkpoints on dev and test sets. \n\n\n\nREPO_PATH=/home/lixiaoya/xiaoy_tf\nexport PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\n\noutput_dir=/xiaoya/mention_proposal_output\nbert_dir=/xiaoya/pretrain_ckpt/uncased_L-2_H-128_A-2\ndata_dir=/xiaoya/corefqa_data/final_overlap_64_2\n\n\nrm -rf ${output_dir}\nmkdir -p ${output_dir}\n\n\n\nCUDA_VISIBLE_DEVICES=3 python3 ${REPO_PATH}/run/run_corefqa.py \\\n--output_dir=${output_dir} \\\n--bert_config_file=${bert_dir}/bert_config_nodropout.json \\\n--init_checkpoint=${bert_dir}/bert_model.ckpt \\\n--vocab_file=${bert_dir}/vocab.txt \\\n--logfile_path=${output_dir}/train.log \\\n--num_epochs=20 \\\n--keep_checkpoint_max=50 \\\n--save_checkpoints_steps=500 \\\n--train_file=${data_dir}/train.64.english.tfrecord \\\n--dev_file=${data_dir}/dev.64.english.tfrecord \\\n--test_file=${data_dir}/test.64.english.tfrecord \\\n--do_train=True \\\n--do_eval=False \\\n--do_predict=False \\\n--learning_rate=1e-5 \\\n--dropout_rate=0.0 \\\n--mention_threshold=0.5 \\\n--hidden_size=128 \\\n--num_docs=5604 \\\n--window_size=64 \\\n--num_window=2 \\\n--max_num_mention=20 \\\n--start_end_share=False \\\n--max_span_width=20 \\\n--max_candidate_mentions=50 \\\n--top_span_ratio=0.2 \\\n--max_top_antecedents=30 \\\n--max_query_len=150 \\\n--max_context_len=150 \\\n--sec_qa_mention_score=False \\\n--use_tpu=False \\\n--seed=2333\n\n\n\n"
},
{
"path": "scripts/models/corefqa_tpu.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# clean code and add comments \n\n\n\nREPO_PATH=/home/xiaoyli1110/xiaoya/Coref-tf\nexport PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\nexport TPU_NAME=tensorflow-tpu\nexport TPU_ZONE=europe-west4-a\nexport GCP_PROJECT=xiaoyli-20-04-274510\n\noutput_dir=gs://europe_mention_proposal/output_bertlarge\nbert_dir=gs://europe_pretrain_mlm/uncased_L-2_H-128_A-2\ndata_dir=gs://europe_corefqa_data/final_overlap_64_2\n\n\n\npython3 ${REPO_PATH}/run/run_corefqa.py \\\n--output_dir=${output_dir} \\\n--bert_config_file=${bert_dir}/bert_config_nodropout.json \\\n--init_checkpoint=${bert_dir}/bert_model.ckpt \\\n--vocab_file=${bert_dir}/vocab.txt \\\n--logfile_path=${output_dir}/train.log \\\n--num_epochs=20 \\\n--keep_checkpoint_max=50 \\\n--save_checkpoints_steps=500 \\\n--train_file=${data_dir}/train.64.english.tfrecord \\\n--dev_file=${data_dir}/dev.64.english.tfrecord \\\n--test_file=${data_dir}/test.64.english.tfrecord \\\n--do_train=True \\\n--do_eval=False \\\n--do_predict=False \\\n--learning_rate=1e-5 \\\n--dropout_rate=0.0 \\\n--mention_threshold=0.5 \\\n--hidden_size=128 \\\n--num_docs=5604 \\\n--window_size=64 \\\n--num_window=2 \\\n--max_num_mention=20 \\\n--start_end_share=False \\\n--max_span_width=20 \\\n--max_candidate_mentions=50 \\\n--top_span_ratio=0.2 \\\n--max_top_antecedents=30 \\\n--max_query_len=150 \\\n--max_context_len=150 \\\n--sec_qa_mention_score=False \\\n--use_tpu=True \\\n--tpu_name=$TPU_NAME \\\n--tpu_zone=$TPU_ZONE \\\n--gcp_project=$GCP_PROJECT \\\n--num_tpu_cores=1 \\\n--seed=2333\n"
},
{
"path": "scripts/models/mention_gpu.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# clean code and add comments \n\n\n\nREPO_PATH=/home/lixiaoya/xiaoy_tf\nexport PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\n\noutput_dir=/xiaoya/mention_proposal_output\nbert_dir=/xiaoya/pretrain_ckpt/uncased_L-2_H-128_A-2\ndata_dir=/xiaoya/corefqa_data/final_overlap_64_2\n\n\nrm -rf ${output_dir}\nmkdir -p ${output_dir}\n\n\n\nCUDA_VISIBLE_DEVICES=3 python3 ${REPO_PATH}/run/run_mention_proposal.py \\\n--output_dir=${output_dir} \\\n--bert_config_file=${bert_dir}/bert_config_nodropout.json \\\n--init_checkpoint=${bert_dir}/bert_model.ckpt \\\n--vocab_file=${bert_dir}/vocab.txt \\\n--logfile_path=${output_dir}/train.log \\\n--num_epochs=20 \\\n--keep_checkpoint_max=50 \\\n--save_checkpoints_steps=500 \\\n--train_file=${data_dir}/train.64.english.tfrecord \\\n--dev_file=${data_dir}/dev.64.english.tfrecord \\\n--test_file=${data_dir}/test.64.english.tfrecord \\\n--do_train=True \\\n--do_eval=False \\\n--do_predict=False \\\n--learning_rate=1e-5 \\\n--dropout_rate=0.0 \\\n--mention_threshold=0.5 \\\n--hidden_size=128 \\\n--num_docs=5604 \\\n--window_size=64 \\\n--num_window=2 \\\n--max_num_mention=20 \\\n--start_end_share=False \\\n--loss_start_ratio=0.3 \\\n--loss_end_ratio=0.3 \\\n--loss_span_ratio=0.3 \\\n--use_tpu=False \\\n--seed=2333\n"
},
{
"path": "scripts/models/mention_tpu.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# clean code and add comments \n\n\n\nREPO_PATH=/home/xiaoyli1110/xiaoya/Coref-tf\nexport PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\nexport TPU_NAME=tensorflow-tpu\nexport TPU_ZONE=europe-west4-a\nexport GCP_PROJECT=xiaoyli-20-04-274510\n\noutput_dir=gs://europe_mention_proposal/output_bertlarge\nbert_dir=gs://europe_pretrain_mlm/uncased_L-2_H-128_A-2\ndata_dir=gs://europe_corefqa_data/final_overlap_64_2\n\n\n\npython3 ${REPO_PATH}/run/run_mention_proposal.py \\\n--output_dir=${output_dir} \\\n--bert_config_file=${bert_dir}/bert_config_nodropout.json \\\n--init_checkpoint=${bert_dir}/bert_model.ckpt \\\n--vocab_file=${bert_dir}/vocab.txt \\\n--logfile_path=${output_dir}/train.log \\\n--num_epochs=20 \\\n--keep_checkpoint_max=50 \\\n--save_checkpoints_steps=500 \\\n--train_file=${data_dir}/train.64.english.tfrecord \\\n--dev_file=${data_dir}/dev.64.english.tfrecord \\\n--test_file=${data_dir}/test.64.english.tfrecord \\\n--do_train=True \\\n--do_eval=False \\\n--do_predict=False \\\n--learning_rate=1e-5 \\\n--dropout_rate=0.0 \\\n--mention_threshold=0.5 \\\n--hidden_size=128 \\\n--num_docs=5604 \\\n--window_size=64 \\\n--num_window=2 \\\n--max_num_mention=20 \\\n--start_end_share=False \\\n--loss_start_ratio=0.3 \\\n--loss_end_ratio=0.3 \\\n--loss_span_ratio=0.3 \\\n--use_tpu=True \\\n--tpu_name=$TPU_NAME \\\n--tpu_zone=$TPU_ZONE \\\n--gcp_project=$GCP_PROJECT \\\n--num_tpu_cores=1 \\\n--seed=2333\n"
},
{
"path": "scripts/models/quoref_tpu.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# finetune the spanbert model on squad 2.0 for data augment. \n\n\n\nREPO_PATH=/home/shannon/coref-tf\nexport TPU_NAME=tf-tpu\nexport PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\nQUOREF_DIR=gs://qa_tasks/quoref\nBERT_DIR=gs://corefqa_output_squad/panbert_large_squad2_2e-5\nOUTPUT_DIR=gs://corefqa_output_quoref/spanbert_large_squad2_best_quoref_3e-5 \n\n\npython3 ${REPO_PATH}/run_quoref.py \\\n--vocab_file=$BERT_DIR/vocab.txt \\\n--bert_config_file=$BERT_DIR/bert_config.json \\\n--init_checkpoint=$BERT_DIR/best_bert_model.ckpt \\\n--do_train=True \\\n--train_file=$QUOREF_DIR/quoref-train-v0.1.json \\\n--do_predict=True \\\n--predict_file=$QUOREF_DIR/quoref-dev-v0.1.json \\\n--train_batch_size=8 \\\n--learning_rate=3e-5 \\\n--num_train_epochs=5 \\\n--max_seq_length=384 \\\n--do_lower_case=False \\\n--doc_stride=128 \\\n--output_dir=${OUTPUT_DIR} \\\n--use_tpu=True \\\n--tpu_name=$TPU_NAME"
},
{
"path": "scripts/models/squad_tpu.sh",
"content": "#!/usr/bin/env bash \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# finetune the spanbert model on squad 2.0 for data augment. \n\n\n\nREPO_PATH=/home/shannon/coref-tf\nexport TPU_NAME=tf-tpu\nexport PYTHONPATH=\"$PYTHONPATH:$REPO_PATH\"\nSQUAD_DIR=gs://qa_tasks/squad2\nBERT_DIR=gs://pretrained_mlm_checkpoint/spanbert_large_tf\nOUTPUT_DIR=gs://corefqa_output_squad/spanbert_large_squad2_2e-5 \n\n\npython3 ${REPO_PATH}/run/run_squad.py \\\n--vocab_file=$BERT_DIR/vocab.txt \\\n--bert_config_file=$BERT_DIR/bert_config.json \\\n--init_checkpoint=$BERT_DIR/bert_model.ckpt \\\n--do_train=True \\\n--train_file=$SQUAD_DIR/train-v2.0.json \\\n--do_predict=True \\\n--predict_file=$SQUAD_DIR/dev-v2.0.json \\\n--train_batch_size=8 \\\n--learning_rate=2e-5 \\\n--num_train_epochs=4.0 \\\n--max_seq_length=384 \\\n--do_lower_case=False \\\n--doc_stride=128 \\\n--output_dir=${OUTPUT_DIR} \\\n--use_tpu=True \\\n--tpu_name=$TPU_NAME \\\n--version_2_with_negative=True"
},
{
"path": "tests/bitwise_and.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# \n\n\nimport tensorflow as tf \n\n\nif __name__ == \"__main__\":\n sess = tf.compat.v1.InteractiveSession()\n lhs = tf.constant([0, 5, 3, 14], dtype=tf.int32)\n rhs = tf.constant([5, 0, 7, 11], dtype=tf.int32)\n\n res = tf.bitwise.bitwise_and(lhs, rhs)\n res.eval()\n # array([ 0, 0, 3, 10], dtype=int32)\n sess.close()\n\n\n"
},
{
"path": "tests/construct_label.py",
"content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*- \n\n\n\n# desc:\n# construct labels \n\n\nimport tensorflow as tf \n\n\n\nif __name__ == \"__main__\":\n sess = tf.compat.v1.InteractiveSession()\n gold_starts = tf.constant([1, 2, 3, 4])\n gold_ends = tf.constant([2, 3, 4, 5])\n num_word = 10 \n gold_mention_sparse_label = tf.stack([gold_starts, gold_ends], axis=1)\n gold_mention_sparse_label.eval()\n gold_span_value = tf.reshape(tf.ones_like(gold_starts, tf.int32), [-1])\n gold_span_shape = tf.constant([num_word, num_word])\n gold_span_label = tf.cast(tf.scatter_nd(gold_mention_sparse_label, gold_span_value, gold_span_shape), tf.int32)\n gold_span_label.eval()\n\n candidate_start = tf.constant([1, 4, 5])\n candidate_end = tf.constant([2, 5, 5])\n candidate_span = tf.stack([candidate_start, candidate_end], axis=1)\n\n gold_span_label = tf.gather_nd(gold_span_label, tf.expand_dims(candidate_span, 1))\n gold_span_label.eval()\n"
},
{
"path": "tests/cumsum.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\"\"\"\ninput_a:\n array([[0, 1, 0, 1, 1],\n [0, 1, 1, 1, 1],\n [0, 1, 1, 0, 1],\n [1, 1, 1, 1, 1],\n [0, 1, 1, 1, 0]], dtype=int32)\ncum_input_b:\n array([[1, 2, 3, 4, 5],\n [1, 2, 3, 4, 5],\n [1, 2, 3, 4, 5],\n [1, 2, 3, 4, 5],\n [1, 2, 3, 4, 5]], dtype=int32)\ninput_c:\n array([[0, 2, 0, 4, 5],\n [0, 2, 3, 4, 5],\n [0, 2, 3, 0, 5],\n [1, 2, 3, 4, 5],\n [0, 2, 3, 4, 0]], dtype=int32)\ninput_c:\n array([[ 1, 2, 3, 4, 5],\n [129, 130, 131, 132, 133],\n [257, 258, 259, 260, 261],\n [385, 386, 387, 388, 389],\n [513, 514, 515, 516, 517]], dtype=int32)\ninput_d:\n array([[ 0, 2, 0, 4, 5],\n [ 0, 130, 131, 132, 133],\n [ 0, 258, 259, 0, 261],\n [385, 386, 387, 388, 389],\n [ 0, 514, 515, 516, 0]], dtype=int32)\nflat_input_d:\n array([ 0, 2, 0, 4, 5, 0, 130, 131, 132, 133, 0, 258, 259,\n 0, 261, 385, 386, 387, 388, 389, 0, 514, 515, 516, 0], dtype=int32)\nboolean_mask:\n array([False, True, False, True, True, False, True, True, True,\n True, False, True, True, False, True, True, True, True,\n True, True, False, True, True, True, False])\ninput_f:\n array([ 2, 4, 5, 130, 131, 132, 133, 258, 259, 261, 385, 386, 387,\n 388, 389, 514, 515, 516], dtype=int32)\n\"\"\"\n\n\n\n\nimport tensorflow as tf \n\n\nif __name__ == \"__main__\":\n sess = tf.compat.v1.InteractiveSession()\n input_a = tf.constant([\n [0, 1, 0, 1, 1], [0, 1, 1, 1, 1], [0, 1, 1, 0, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0]])\n ones_input_b = tf.ones_like(input_a, tf.int32)\n cum_input_b = tf.math.cumsum(ones_input_b, axis=1)\n cum_input_b.eval()\n # input_c = tf.math.multiply(cum_input_b, input_a)\n # input_c.eval()\n seq_len = 128\n offset = tf.tile(tf.expand_dims(tf.range(5) * 128, 1), [1, 5])\n offset.eval()\n input_e = offset + cum_input_b \n input_e.eval()\n\n input_d = tf.math.multiply(input_e, input_a)\n input_d.eval()\n flat_input_d = tf.reshape(input_d, [-1])\n flat_input_d.eval()\n\n boolean_mask = tf.math.greater(flat_input_d, tf.zeros_like(flat_input_d, tf.int32))\n boolean_mask.eval()\n\n input_f = tf.boolean_mask(flat_input_d, boolean_mask)\n input_f.eval()\n\n sess.close()\n\n\n"
},
{
"path": "tests/gather.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n\n# author: xiaoy li \n\n\nimport tensorflow as tf \n\n\n\nif __name__ == \"__main__\":\n sess = tf.compat.v1.InteractiveSession()\n lhs = tf.zeros((4, 3))\n\n slice_lhs = tf.gather(lhs, 1)\n # slice_lhs_nd = tf.gather_nd(lhs, 1)\n\n slice_lhs = tf.gather(lhs, [1, 2])\n\n slice_lhs.eval()\n # slice_lhs_nd.eval()\n # array([ 0, 0, 3, 10], dtype=int32)\n sess.close()"
},
{
"path": "tests/model_fn.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# test config in model fn builder \n\n\n\n\ndef model_fn(config):\n\n def mention_proposal_fn():\n print(\"the number of document is : \")\n print(config.document_number)\n print(config.number_window_size)\n\n return mention_proposal_fn \n\n\nclass Config:\n number_window_size = 2 \n document_number = 3 \n\n\n\n\nif __name__ == \"__main__\":\n config = Config()\n get_model_fn = model_fn(config)\n get_model_fn()"
},
{
"path": "tests/tile_repeat.py",
"content": "#!/usr/bin/env python3 \n \nimport numpy as np \nimport tensorflow as tf \n\n\na_np = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [2, 3, 4], [5, 6, 7], [8, 9, 10]])\n\nprint(a_np.shape)\n# exit()\n\ndef shape(x, dim):\n return x.get_shape()[dim].value or tf.shape(x)[dim]\n\n\nif __name__ == \"__main__\":\n\n original_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [2, 3, 4], [5, 6, 7], [8, 9, 10]])\n sess = tf.compat.v1.InteractiveSession()\n start_scores = tf.convert_to_tensor(tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9], [2, 3, 4], [5, 6, 7], [8, 9, 10]]))\n print(tf.shape(start_scores))\n # exit()\n expand_scores = tf.tile(tf.expand_dims(start_scores, 2), [1, 1, 3])\n # (6, 3, 3)\n print(expand_scores.eval())\n print(shape(expand_scores, 0))\n print(shape(expand_scores, 1))\n print(shape(expand_scores, 2))\n print(\"=*=\"*20)\n # tf.convert_to_tensor(data_np, np.float32)\n # ndarray_scores = tf.make_ndarray(expand_scores)\n # ndarray_scores = tf.convert_to_tensor(expand_scores, np.int32)\n # print(ndarray_scores)\n # exit()\n ndarray_scores = np.array([[[1, 1, 1], [ 2 , 2 , 2], [ 3 , 3 , 3]],\n [[ 4 , 4 , 4],[ 5 , 5 , 5],[ 6 , 6 , 6]],\n [[ 7 , 7 , 7],[ 8 , 8 , 8],[ 9 , 9 , 9]],\n [[ 2 , 2 , 2], [ 3 , 3 , 3], [ 4 , 4 , 4]],\n [[ 5 , 5 , 5], [ 6 , 6 , 6], [ 7 , 7 , 7]],\n [[ 8 , 8 , 8], [ 9 , 9 , 9], [10 , 10 ,10]]])\n print(\"$=\"*20)\n print(\"test_a is : {}\".format(str(ndarray_scores[2, 2, 2])))\n print(\"test_b is : {}\".format(str(original_array[2, 2])))\n print(\"^-\"*20)\n print(\"test_a is : {}\".format(str(ndarray_scores[2, 1, 1])))\n print(\"test_b is : {}\".format(str(original_array[2, 1])))\n print(\"^-\"*20)\n print(\"test_a is : {}\".format(str(ndarray_scores[2, 0, 0])))\n print(\"test_b is : {}\".format(str(original_array[2, 0])))\n sess.close() \n # span_scores[k][i][j] = start_scores[k][i] + end_scores[k][j]\n # start_scores[k][i][j] = start_scores[k][i]\n # end_scores[k][i][j] = end_scores[k][j]\n\n # [[1, 2, 3], [4, 5, 6], [7, 8, 9], [2, 3, 4], [5, 6, 7], [8, 9, 10]]\n\n \"\"\"\n [[[1, 1, 1], [ 2 , 2 , 2], [ 3 , 3 , 3]],\n [[ 4 , 4 , 4],[ 5 , 5 , 5],[ 6 , 6 , 6]],\n [[ 7 , 7 , 7],[ 8 , 8 , 8],[ 9 , 9 , 9]],\n [[ 2 , 2 , 2], [ 3 , 3 , 3], [ 4 , 4 , 4]],\n [[ 5 , 5 , 5], [ 6 , 6 , 6], [ 7 , 7 , 7]],\n [[ 8 , 8 , 8], [ 9 , 9 , 9], [10 , 10 ,10]]]\n \"\"\"\n\n"
},
{
"path": "tests/tpu_operation.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# descripiton:\n# test math operations in tpu \n\n\nimport tensorflow as tf \nfrom tensorflow.contrib import tpu\nfrom tensorflow.contrib.cluster_resolver import TPUClusterResolver\n\n\n\nTPU_NAME = \"tensorflow-tpu\"\nTPU_ZONE = \"us-central1-f\"\nGCP_PROJECT = \"xiaoyli-20-04-274510\"\n\n\n\nif __name__ == \"__main__\":\n tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(TPU_NAME, zone=TPU_ZONE, project=GCP_PROJECT) \n # tpu_cluster_resolver = TPUClusterResolver(tpu=['tensorflow-tpu']).get_master()\n tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)\n tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)\n\n scores = tf.constant([1.0, 2.3, 3.2, 4.3, 1.5, 1.8, 98, 2.9])\n k = 2\n\n def test_top_k():\n top_scores, top_index = tf.nn.top_k(scores, k)\n return top_scores, top_index \n\n test_op = test_top_k\n\n # with tf.compat.v1.InteractiveSession(tpu_cluster_resolver) as sess:\n with tf.compat.v1.Session(tpu_cluster_resolver) as sess:\n sess.run(tpu.initialize_system())\n\n scores = tf.constant([1.0, 2.3, 3.2, 4.3, 1.5, 1.8, 98, 2.9])\n k = 2\n print(\"ALL Devices: \", tf.config.experimental_list_devices())\n\n top_scores, top_index = tf.nn.top_k(scores, k) \n\n print(top_scores.eval())\n print(top_index.eval())\n\n sess.run(tpu.shutdown_system())\n\n\n\n\n\n"
},
{
"path": "utils/load_pytorch_to_tf.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\n# author: xiaoy li \n# description:\n# transform pretrained model checkpoints from [pytorch *.bin] to [tensorflow *.ckpt]\n# Reference:\n# https://github.com/mandarjoshi90/coref/blob/master/pytorch_to_tf.py\n\n\nimport numpy as np\nimport torch\nimport tensorflow as tf\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import variable_scope as vs\n\n\ntensors_to_transpose = (\n \"dense/kernel\",\n \"attention/self/query\",\n \"attention/self/key\",\n \"attention/self/value\"\n)\n\nvar_map = (\n ('layer.', 'layer_'),\n ('word_embeddings.weight', 'word_embeddings'),\n ('position_embeddings.weight', 'position_embeddings'),\n ('token_type_embeddings.weight', 'token_type_embeddings'),\n ('.', '/'),\n ('LayerNorm/weight', 'LayerNorm/gamma'),\n ('LayerNorm/bias', 'LayerNorm/beta'),\n ('weight', 'kernel')\n)\n\n\ndef to_tf_var_name(name: str):\n for patt, repl in iter(var_map):\n name = name.replace(patt, repl)\n return '{}'.format(name)\n\n\ndef my_convert_keys(model):\n converted = {}\n for k_pt, v in model.items():\n k_tf = to_tf_var_name(k_pt)\n converted[k_tf] = v\n return converted\n\n\ndef load_from_pytorch_checkpoint(checkpoint, assignment_map):\n pytorch_model = torch.load(checkpoint, map_location='cpu')\n pt_model_with_tf_keys = my_convert_keys(pytorch_model)\n for _, name in assignment_map.items():\n store_vars = vs._get_default_variable_store()._vars\n var = store_vars.get(name, None)\n assert var is not None\n if name not in pt_model_with_tf_keys:\n print('WARNING:', name, 'not found in original model.')\n continue\n array = pt_model_with_tf_keys[name].cpu().numpy()\n if any([x in name for x in tensors_to_transpose]):\n array = array.transpose()\n assert tuple(var.get_shape().as_list()) == tuple(array.shape)\n init_value = ops.convert_to_tensor(array, dtype=np.float32)\n var._initial_value = init_value\n var._initializer_op = var.assign(init_value)\n\n\ndef print_vars(pytorch_ckpt, tf_ckpt):\n tf_vars = tf.train.list_variables(tf_ckpt)\n tf_vars = {k: v for (k, v) in tf_vars}\n pytorch_model = torch.load(pytorch_ckpt)\n pt_model_with_tf_keys = my_convert_keys(pytorch_model)\n only_pytorch, only_tf, common = [], [], []\n tf_only = set(tf_vars.keys())\n for k, v in pt_model_with_tf_keys.items():\n if k in tf_vars:\n common.append(k)\n tf_only.remove(k)\n else:\n only_pytorch.append(k)\n print('-------------------')\n print('Common', len(common))\n for k in common:\n array = pt_model_with_tf_keys[k].cpu().numpy()\n if any([x in k for x in tensors_to_transpose]):\n array = array.transpose()\n tf_shape = tuple(tf_vars[k])\n pt_shape = tuple(array.shape)\n if tf_shape != pt_shape:\n print(k, tf_shape, pt_shape)\n print('-------------------')\n print('Pytorch only', len(only_pytorch))\n for k in only_pytorch:\n print(k, pt_model_with_tf_keys[k].size())\n print('-------------------')\n print('TF only', len(tf_only))\n for k in tf_only:\n print(k, tf_vars[k])\n\n\n"
},
{
"path": "utils/metrics.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\nimport numpy as np\nfrom collections import Counter\nfrom scipy.optimize import linear_sum_assignment\n\n\ndef mention_proposal_prediction(config, current_doc_result, concat_only=True):\n \"\"\"\n current_doc_result: \n \"total_loss\": total_loss,\n \"start_scores\": start_scores,\n \"start_gold\": gold_starts,\n \"end_gold\": gold_ends,\n \"end_scores\": end_scores, \n \"span_scores\": span_scores, \n \"span_gold\": span_mention\n\n \"\"\"\n\n span_scores = current_doc_result[\"span_scores\"]\n span_gold = current_doc_result[\"span_gold\"] \n\n if concat_only:\n scores = span_scores\n else:\n start_scores = current_doc_result[\"start_scores\"], \n end_scores = current_doc_result[\"end_scores\"] \n # start_scores = tf.tile(tf.expand_dims(start_scores, 2), [1, 1, config[\"max_segment_len\"]])\n start_scores = np.tile(np.expand_dims(start_scores, axis=2), (1, 1, config[\"max_segment_len\"]))\n end_scores = np.tile(np.expand_dims(end_scores, axis=2), (1, 1, config[\"max_segment_len\"]))\n start_scores = np.reshape(start_scores, [-1, config[\"max_segment_len\"], config[\"max_segment_len\"]])\n end_scores = np.reshape(end_scores, [-1, config[\"max_segment_len\"], config[\"max_segment_len\"]])\n\n # end_scores -> max_training_sent, max_segment_len \n scores = (start_scores + end_scores + span_scores)/3\n\n pred_span_label = scores >= 0.5\n pred_span_label = np.reshape(pred_span_label, [-1])\n gold_span_label = np.reshape(span_gold, [-1])\n\n return pred_span_label, gold_span_label\n\n\ndef f1(p_num, p_den, r_num, r_den, beta=1):\n p = 0 if p_den == 0 else p_num / float(p_den)\n r = 0 if r_den == 0 else r_num / float(r_den)\n return 0 if p + r == 0 else (1 + beta * beta) * p * r / (beta * beta * p + r)\n\n\nclass CorefEvaluator(object):\n def __init__(self):\n self.evaluators = [Evaluator(m) for m in (muc, b_cubed, ceafe)]\n\n def update(self, predicted, gold, mention_to_predicted, mention_to_gold):\n for e in self.evaluators:\n e.update(predicted, gold, mention_to_predicted, mention_to_gold)\n\n def get_f1(self):\n return sum(e.get_f1() for e in self.evaluators) / len(self.evaluators)\n\n def get_recall(self):\n return sum(e.get_recall() for e in self.evaluators) / len(self.evaluators)\n\n def get_precision(self):\n return sum(e.get_precision() for e in self.evaluators) / len(self.evaluators)\n\n def get_prf(self):\n return self.get_precision(), self.get_recall(), self.get_f1()\n\n\nclass Evaluator(object):\n def __init__(self, metric, beta=1):\n self.p_num = 0\n self.p_den = 0\n self.r_num = 0\n self.r_den = 0\n self.metric = metric\n self.beta = beta\n\n def update(self, predicted, gold, mention_to_predicted, mention_to_gold):\n if self.metric == ceafe:\n pn, pd, rn, rd = self.metric(predicted, gold)\n else:\n pn, pd = self.metric(predicted, mention_to_gold)\n rn, rd = self.metric(gold, mention_to_predicted)\n self.p_num += pn\n self.p_den += pd\n self.r_num += rn\n self.r_den += rd\n\n def get_f1(self):\n return f1(self.p_num, self.p_den, self.r_num, self.r_den, beta=self.beta)\n\n def get_recall(self):\n return 0 if self.r_num == 0 else self.r_num / float(self.r_den)\n\n def get_precision(self):\n return 0 if self.p_num == 0 else self.p_num / float(self.p_den)\n\n def get_prf(self):\n return self.get_precision(), self.get_recall(), self.get_f1()\n\n def get_counts(self):\n return self.p_num, self.p_den, self.r_num, self.r_den\n\n\ndef evaluate_documents(documents, metric, beta=1):\n evaluator = Evaluator(metric, beta=beta)\n for document in documents:\n evaluator.update(document)\n return evaluator.get_precision(), evaluator.get_recall(), evaluator.get_f1()\n\n\ndef b_cubed(clusters, mention_to_gold):\n num, dem = 0, 0\n\n for c in clusters:\n if len(c) == 1:\n continue\n\n gold_counts = Counter()\n correct = 0\n for m in c:\n if m in mention_to_gold:\n gold_counts[tuple(mention_to_gold[m])] += 1\n for c2, count in gold_counts.items():\n if len(c2) != 1:\n correct += count * count\n\n num += correct / float(len(c))\n dem += len(c)\n\n return num, dem\n\n\ndef muc(clusters, mention_to_gold):\n tp, p = 0, 0\n for c in clusters:\n p += len(c) - 1\n tp += len(c)\n linked = set()\n for m in c:\n if m in mention_to_gold:\n linked.add(mention_to_gold[m])\n else:\n tp -= 1\n tp -= len(linked)\n return tp, p\n\n\ndef phi4(c1, c2):\n return 2 * len([m for m in c1 if m in c2]) / float(len(c1) + len(c2))\n\n\ndef ceafe(clusters, gold_clusters):\n clusters = [c for c in clusters if len(c) != 1]\n scores = np.zeros((len(gold_clusters), len(clusters)))\n for i in range(len(gold_clusters)):\n for j in range(len(clusters)):\n scores[i, j] = phi4(gold_clusters[i], clusters[j])\n row_ind, col_ind = linear_sum_assignment(-scores)\n similarity = sum(scores[row_ind, col_ind])\n return similarity, len(clusters), similarity, len(gold_clusters)\n\n\ndef lea(clusters, mention_to_gold):\n num, dem = 0, 0\n\n for c in clusters:\n if len(c) == 1:\n continue\n\n common_links = 0\n all_links = len(c) * (len(c) - 1) / 2.0\n for i, m in enumerate(c):\n if m in mention_to_gold:\n for m2 in c[i + 1:]:\n if m2 in mention_to_gold and mention_to_gold[m] == mention_to_gold[m2]:\n common_links += 1\n\n num += len(c) * common_links / float(all_links)\n dem += len(c)\n\n return num, dem\n"
},
{
"path": "utils/radam.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\nimport tensorflow as tf\nfrom tensorflow.python import (\n ops, math_ops, state_ops, control_flow_ops, resource_variable_ops)\nfrom tensorflow.python.training.optimizer import Optimizer\n\n__all__ = ['RAdam']\n\n\nclass RAdam(Optimizer):\n \"\"\"Rectified Adam (RAdam) optimizer.\n According to the paper\n [On The Variance Of The Adaptive Learning Rate And Beyond](https://arxiv.org/pdf/1908.03265v1.pdf).\n \"\"\"\n\n def __init__(self,\n learning_rate=0.001,\n beta1=0.9,\n beta2=0.999,\n epsilon=1e-8,\n amsgrad=False,\n use_locking=False,\n name='RAdam'):\n r\"\"\"Construct a new Rectified Adam optimizer.\n Args:\n learning_rate: A Tensor or a floating point value. The learning rate.\n beta1: A float value or a constant float tensor. The exponential decay\n rate for the 1st moment estimates.\n beta2: A float value or a constant float tensor. The exponential decay\n rate for the 2nd moment estimates.\n epsilon: A small constant for numerical stability. This epsilon is\n \"epsilon hat\" in the Kingma and Ba paper (in the formula just before\n Section 2.1), not the epsilon in Algorithm 1 of the paper.\n amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from\n the paper \"On the Convergence of Adam and beyond\".\n use_locking: If `True` use locks for update operations.\n name: Optional name for the operations created when applying gradients.\n Defaults to \"Adam\". @compatibility(eager) When eager execution is\n enabled, `learning_rate`, `beta1`, `beta2`, and `epsilon` can each be\n a callable that takes no arguments and returns the actual value to use.\n This can be useful for changing these values across different\n invocations of optimizer functions. @end_compatibility\n \"\"\"\n\n super(RAdam, self).__init__(use_locking, name)\n self._lr = learning_rate\n self._beta1 = beta1\n self._beta2 = beta2\n self._epsilon = epsilon\n self._amsgrad = amsgrad\n\n def _get_beta_accumulators(self):\n with ops.init_scope():\n graph = ops.get_default_graph()\n return (self._get_non_slot_variable(\"beta1_power\", graph=graph),\n self._get_non_slot_variable(\"beta2_power\", graph=graph),\n )\n\n def _get_niter(self):\n with ops.init_scope():\n graph = ops.get_default_graph()\n return self._get_non_slot_variable(\"niter\", graph=graph)\n\n def _create_slots(self, var_list):\n first_var = min(var_list, key=lambda x: x.name)\n self._create_non_slot_variable(\n initial_value=self._beta1, name=\"beta1_power\", colocate_with=first_var)\n self._create_non_slot_variable(\n initial_value=self._beta2, name=\"beta2_power\", colocate_with=first_var)\n self._create_non_slot_variable(\n initial_value=1, name=\"niter\", colocate_with=first_var)\n for var in var_list:\n self._zeros_slot(var, 'm', self._name)\n self._zeros_slot(var, 'v', self._name)\n if self._amsgrad:\n for var in var_list:\n self._zeros_slot(var, 'vhat', self._name)\n\n def _prepare(self):\n learning_rate = self._call_if_callable(self._lr)\n beta1 = self._call_if_callable(self._beta1)\n beta2 = self._call_if_callable(self._beta2)\n epsilon = self._call_if_callable(self._epsilon)\n\n self._lr_t = ops.convert_to_tensor(learning_rate, name=\"learning_rate\")\n self._beta1_t = ops.convert_to_tensor(beta1, name=\"beta1\")\n self._beta2_t = ops.convert_to_tensor(beta2, name=\"beta2\")\n self._epsilon_t = ops.convert_to_tensor(epsilon, name=\"epsilon\")\n\n def _apply_dense_shared(self, grad, var):\n var_dtype = var.dtype.base_dtype\n beta1_power, beta2_power = self._get_beta_accumulators()\n beta1_power = math_ops.cast(beta1_power, var_dtype)\n beta2_power = math_ops.cast(beta2_power, var_dtype)\n niter = self._get_niter()\n niter = math_ops.cast(niter, var_dtype)\n lr_t = math_ops.cast(self._lr_t, var_dtype)\n beta1_t = math_ops.cast(self._beta1_t, var_dtype)\n beta2_t = math_ops.cast(self._beta2_t, var_dtype)\n epsilon_t = math_ops.cast(self._epsilon_t, var_dtype)\n\n sma_inf = 2.0 / (1.0 - beta2_t) - 1.0\n sma_t = sma_inf - 2.0 * niter * beta2_power / (1.0 - beta2_power)\n\n m = self.get_slot(var, 'm')\n m_t = state_ops.assign(m,\n beta1_t * m + (1.0 - beta1_t) * grad,\n use_locking=self._use_locking)\n m_corr_t = m_t / (1.0 - beta1_power)\n\n v = self.get_slot(var, 'v')\n v_t = state_ops.assign(v,\n beta2_t * v + (1.0 - beta2_t) * math_ops.square(grad),\n use_locking=self._use_locking)\n\n if self._amsgrad:\n vhat = self.get_slot(var, 'vhat')\n vhat_t = state_ops.assign(vhat,\n math_ops.maximum(vhat, v_t),\n use_locking=self._use_locking)\n v_corr_t = math_ops.sqrt(vhat_t / (1.0 - beta2_power) + epsilon_t)\n else:\n v_corr_t = math_ops.sqrt(v_t / (1.0 - beta2_power) + epsilon_t)\n\n r_t = math_ops.sqrt((sma_t - 4.0) / (sma_inf - 4.0) *\n (sma_t - 2.0) / (sma_inf - 2.0) *\n sma_inf / sma_t)\n\n var_t = tf.where(sma_t > 5.0, r_t * m_corr_t / v_corr_t, m_corr_t)\n\n var_update = state_ops.assign_sub(var,\n lr_t * var_t,\n use_locking=self._use_locking)\n\n updates = [var_update, m_t, v_t]\n if self._amsgrad:\n updates.append(vhat_t)\n return control_flow_ops.group(*updates)\n\n def _apply_dense(self, grad, var):\n return self._apply_dense_shared(grad, var)\n\n def _resource_apply_dense(self, grad, var):\n return self._apply_dense_shared(grad, var.handle)\n\n def _apply_sparse_shared(self, grad, var, indices, scatter_add):\n var_dtype = var.dtype.base_dtype\n beta1_power, beta2_power = self._get_beta_accumulators()\n beta1_power = math_ops.cast(beta1_power, var_dtype)\n beta2_power = math_ops.cast(beta2_power, var_dtype)\n niter = self._get_niter()\n niter = math_ops.cast(niter, var_dtype)\n lr_t = math_ops.cast(self._lr_t, var_dtype)\n beta1_t = math_ops.cast(self._beta1_t, var_dtype)\n beta2_t = math_ops.cast(self._beta2_t, var_dtype)\n epsilon_t = math_ops.cast(self._epsilon_t, var_dtype)\n\n sma_inf = 2.0 / (1.0 - beta2_t) - 1.0\n sma_t = sma_inf - 2.0 * niter * beta2_power / (1.0 - beta2_power)\n\n m = self.get_slot(var, 'm')\n m_t = state_ops.assign(m, beta1_t * m, use_locking=self._use_locking)\n with ops.control_dependencies([m_t]):\n m_t = scatter_add(m, indices, grad * (1 - beta1_t))\n m_corr_t = m_t / (1.0 - beta1_power)\n\n v = self.get_slot(var, 'v')\n v_t = state_ops.assign(v, beta2_t * v, use_locking=self._use_locking)\n with ops.control_dependencies([v_t]):\n v_t = scatter_add(v, indices, (1.0 - beta2_t) * math_ops.square(grad))\n\n if self._amsgrad:\n vhat = self.get_slot(var, 'vhat')\n vhat_t = state_ops.assign(vhat,\n math_ops.maximum(vhat, v_t),\n use_locking=self._use_locking)\n v_corr_t = math_ops.sqrt(vhat_t / (1.0 - beta2_power) + epsilon_t)\n else:\n v_corr_t = math_ops.sqrt(v_t / (1.0 - beta2_power) + epsilon_t)\n\n r_t = math_ops.sqrt((sma_t - 4.0) / (sma_inf - 4.0) *\n (sma_t - 2.0) / (sma_inf - 2.0) *\n sma_inf / sma_t)\n\n var_t = tf.where(sma_t > 5.0, r_t * m_corr_t / v_corr_t, m_corr_t)\n\n var_update = state_ops.assign_sub(var,\n lr_t * var_t,\n use_locking=self._use_locking)\n\n updates = [var_update, m_t, v_t]\n if self._amsgrad:\n updates.append(vhat_t)\n return control_flow_ops.group(*updates)\n\n def _apply_sparse(self, grad, var):\n return self._apply_sparse_shared(\n grad.values,\n var,\n grad.indices,\n lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda\n x,\n i,\n v,\n use_locking=self._use_locking))\n\n def _resource_apply_sparse(self, grad, var, indices):\n return self._apply_sparse_shared(grad, var, indices,\n self._resource_scatter_add)\n\n def _resource_scatter_add(self, x, i, v):\n with ops.control_dependencies(\n [resource_variable_ops.resource_scatter_add(x.handle, i, v)]):\n return x.value()\n\n def _finish(self, update_ops, name_scope):\n # Update the power accumulators.\n with ops.control_dependencies(update_ops):\n beta1_power, beta2_power = self._get_beta_accumulators()\n niter = self._get_niter()\n with ops.colocate_with(beta1_power):\n update_beta1 = beta1_power.assign(\n beta1_power * self._beta1_t, use_locking=self._use_locking)\n update_beta2 = beta2_power.assign(\n beta2_power * self._beta2_t, use_locking=self._use_locking)\n update_niter = niter.assign(\n niter + 1, use_locking=self._use_locking)\n return control_flow_ops.group(\n *update_ops + [update_beta1, update_beta2, update_niter], name=name_scope)\n"
},
{
"path": "utils/util.py",
"content": "#!/usr/bin/env python3 \n# -*- coding: utf-8 -*- \n\n\n\nimport codecs\nimport collections\nimport errno\nimport os\nimport shutil\nimport pyhocon\nimport tensorflow as tf\nfrom models import corefqa\nfrom models import mention_proposal \n\n\nrepo_path = \"/\".join(os.path.realpath(__file__).split(\"/\")[:-2])\n\n\ndef get_model(config, model_sign=\"corefqa\"): \n if model_sign == \"corefqa\":\n return corefqa.CorefQAModel(config)\n else:\n return mention_proposal.MentionProposalModel(config)\n\n\ndef initialize_from_env(eval_test=False, config_params=\"train_spanbert_base\", config_file=\"experiments_tinybert.conf\", use_tpu=False, print_info=False):\n if not use_tpu:\n print(\"loading experiments.conf ... \")\n config = pyhocon.ConfigFactory.parse_file(os.path.join(repo_path, config_file)) \n else: \n print(\"loading experiments_tpu.conf ... \")\n config = pyhocon.ConfigFactory.parse_file(os.path.join(repo_path, config_file))\n\n config = config[config_params] \n\n if print_info:\n tf.logging.info(\"%*%\"*20)\n tf.logging.info(\"%*%\"*20)\n tf.logging.info(\"%%%%%%%% Configs are showed as follows : %%%%%%%%\")\n for tmp_key, tmp_value in config.items():\n tf.logging.info(str(tmp_key) + \" : \" + str(tmp_value)) \n \n tf.logging.info(\"%*%\"*20)\n tf.logging.info(\"%*%\"*20)\n\n config[\"log_dir\"] = mkdirs(os.path.join(config[\"log_root\"], config_params))\n\n if print_info:\n tf.logging.info(pyhocon.HOCONConverter.convert(config, \"hocon\"))\n return config\n\n\ndef copy_checkpoint(source, target):\n for ext in (\".index\", \".data-00000-of-00001\"):\n shutil.copyfile(source + ext, target + ext)\n\n\ndef make_summary(value_dict):\n return tf.Summary(value=[tf.Summary.Value(tag=k, simple_value=v) for k, v in value_dict.items()])\n\n\ndef flatten(l):\n return [item for sublist in l for item in sublist]\n\n\ndef set_gpus(*gpus):\n # pass\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \",\".join(str(g) for g in gpus)\n print(\"Setting CUDA_VISIBLE_DEVICES to: {}\".format(os.environ[\"CUDA_VISIBLE_DEVICES\"]))\n\n\ndef mkdirs(path):\n try:\n os.makedirs(path)\n except OSError as exception:\n if exception.errno != errno.EEXIST:\n raise\n return path\n\n\ndef load_char_dict(char_vocab_path):\n vocab = [u\"\"]\n with codecs.open(char_vocab_path, encoding=\"utf-8\") as f:\n vocab.extend(l.strip() for l in f.readlines())\n char_dict = collections.defaultdict(int)\n char_dict.update({c: i for i, c in enumerate(vocab)})\n return char_dict\n\n\ndef maybe_divide(x, y):\n return 0 if y == 0 else x / float(y)\n\n\n\ndef shape(x, dim):\n return x.get_shape()[dim].value or tf.shape(x)[dim]\n\n\ndef ffnn(inputs, num_hidden_layers, hidden_size, output_size, dropout,\n output_weights_initializer=tf.truncated_normal_initializer(stddev=0.02),\n hidden_initializer=tf.truncated_normal_initializer(stddev=0.02)):\n if len(inputs.get_shape()) > 3:\n raise ValueError(\"FFNN with rank {} not supported\".format(len(inputs.get_shape())))\n current_inputs = inputs\n hidden_weights = tf.get_variable(\"hidden_weights\", [hidden_size, output_size],\n initializer=hidden_initializer)\n hidden_bias = tf.get_variable(\"hidden_bias\", [output_size], initializer=tf.zeros_initializer())\n current_outputs = tf.nn.relu(tf.nn.xw_plus_b(current_inputs, hidden_weights, hidden_bias))\n\n return current_outputs\n\n\ndef batch_gather(emb, indices):\n batch_size = shape(emb, 0)\n seqlen = shape(emb, 1)\n if len(emb.get_shape()) > 2:\n emb_size = shape(emb, 2)\n else:\n emb_size = 1\n flattened_emb = tf.reshape(emb, [batch_size * seqlen, emb_size]) # [batch_size * seqlen, emb]\n offset = tf.expand_dims(tf.range(batch_size) * seqlen, 1) # [batch_size, 1]\n gathered = tf.gather(flattened_emb, indices + offset) # [batch_size, num_indices, emb]\n if len(emb.get_shape()) == 2:\n gathered = tf.squeeze(gathered, 2) # [batch_size, num_indices]\n return gathered\n\n"
}
]