[ { "path": ".github/scripts/release.py", "content": "#!/usr/bin/env python3\nimport json\nimport subprocess\n\n\ndef get_last_version() -> str:\n \"\"\"Return the version number of the last release.\"\"\"\n json_string = (\n subprocess.run(\n [\"gh\", \"release\", \"view\", \"--json\", \"tagName\"],\n check=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n .stdout.decode(\"utf8\")\n .strip()\n )\n\n return json.loads(json_string)[\"tagName\"]\n\n\ndef bump_patch_number(version_number: str) -> str:\n \"\"\"Return a copy of `version_number` with the patch number incremented.\"\"\"\n major, minor, patch = version_number.split(\".\")\n return f\"{major}.{minor}.{int(patch) + 1}\"\n\n\ndef create_new_patch_release():\n \"\"\"Create a new patch release on GitHub.\"\"\"\n try:\n last_version_number = get_last_version()\n except subprocess.CalledProcessError as err:\n if err.stderr.decode(\"utf8\").startswith(\"HTTP 404:\"):\n # The project doesn't have any releases yet.\n new_version_number = \"0.0.1\"\n else:\n raise\n else:\n new_version_number = bump_patch_number(last_version_number)\n\n subprocess.run(\n [\"gh\", \"release\", \"create\", \"--generate-notes\", new_version_number],\n check=True,\n )\n\n\nif __name__ == \"__main__\":\n create_new_patch_release()\n" }, { "path": ".github/workflows/python-publish.yml", "content": "name: Publish to PyPI.org\non:\n release:\n types: [published]\njobs:\n pypi:\n runs-on: ubuntu-latest\n steps:\n - name: Checkout\n uses: actions/checkout@v3\n with:\n fetch-depth: 0\n - run: python3 -m pip install --upgrade build && python3 -m build\n - name: Publish package\n uses: pypa/gh-action-pypi-publish@release/v1\n with:\n password: ${{ secrets.PYPI_API_TOKEN }}\n" }, { "path": ".github/workflows/release.yml", "content": "name: Create a new patch release\non: workflow_dispatch\njobs:\n github:\n runs-on: ubuntu-latest\n steps:\n - name: Checkout\n uses: actions/checkout@v3\n - name: Create new patch release\n run: .github/scripts/release.py\n env:\n GITHUB_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}\n" }, { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\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.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n" }, { "path": "CITATION.cff", "content": "cff-version: 1.1.0\nmessage: \"If you use this work, please cite it as below.\"\nauthors:\n - family-names: \"Sileo\"\n given-names: \"Damien\"\ntitle: \"tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation\"\nversion: \"1.0.0\"\ndate-released: 2023-01-01\nurl: \"https://arxiv.org/abs/2301.05948\"\n" }, { "path": "LICENSE", "content": "Attribution 4.0 International\n\n=======================================================================\n\nCreative Commons Corporation (\"Creative Commons\") is not a law firm and\ndoes not provide legal services or legal advice. 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For\nthe avoidance of doubt, this paragraph does not form part of the\npublic licenses.\n\nCreative Commons may be contacted at creativecommons.org.\n" }, { "path": "README.md", "content": "## tasksource ![](https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/5fc0bcb41160c47d1d43856b/j06-U5e2Tifi2xOnTudqS.jpeg?w=20&h=20&f=face) 600+ curated datasets and preprocessings for instant and interchangeable use\n\nHuggingface Datasets is an excellent library, but it lacks standardization, and datasets often require preprocessing work to be used interchangeably.\n`tasksource` streamlines interchangeable datasets usage to scale evaluation or multi-task learning.\n\nEach dataset is standardized to a `MultipleChoice`, `Classification`, or `TokenClassification` template with canonical fields. We focus on discriminative tasks (= with negative examples or classes) for our annotations but also provide a `SequenceToSequence` template. All implemented preprocessings are in [tasks.py](https://github.com/sileod/tasksource/blob/main/src/tasksource/tasks.py) or [tasks.md](https://github.com/sileod/tasksource/blob/main/tasks.md). A preprocessing is a function that accepts a dataset and returns the standardized dataset. Preprocessing code is concise and human-readable.\n\n### Installation and usage:\n`pip install tasksource`\n```python\nfrom tasksource import list_tasks, load_task\ndf = list_tasks(multilingual=False) # takes some time\n\nfor id in df[df.task_type==\"MultipleChoice\"].id:\n dataset = load_task(id) # all yielded datasets can be used interchangeably\n```\n\nBrowse the 500+ curated tasks in tasks.md (200+ MultipleChoice tasks, 200+ Classification tasks), and feel free to request a new task. Datasets are downloaded to `$HF_DATASETS_CACHE` (like any Hugging Face dataset), so ensure you have more than 100GB of space available.\n\nYou can now also use:\n```python\nload_dataset(\"tasksource/data\", \"glue/rte\",max_rows=30_000)\n```\n\n### Pretrained models:\n\nText encoder pretrained on tasksource reached state-of-the-art results: [🤗/deberta-v3-base-tasksource-nli](https://hf.co/sileod/deberta-v3-base-tasksource-nli)\n\nTasksource pretraining is notably helpful for RLHF reward modeling or any kind of classification, including zero-shot. You can also find a large and a multilingual version.\n\n### tasksource-instruct\n\nThe repo also contains some recasting code to convert tasksource datasets to instructions, providing one of the richest instruction-tuning datasets:\n[🤗/tasksource-instruct-v0](https://hf.co/datasets/tasksource/tasksource-instruct-v0)\n\n\n### tasksource-label-nli\n\nWe also recast all classification tasks as natural language inference, to improve entailment-based zero-shot classification detection:\n[🤗/zero-shot-label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli)\n\n### Write and use custom preprocessings\n\n```python\nfrom tasksource import MultipleChoice\n\ncodah = MultipleChoice('question_propmt',choices_list='candidate_answers',\n labels='correct_answer_idx',\n dataset_name='codah', config_name='codah')\n \nwinogrande = MultipleChoice('sentence',['option1','option2'],'answer',\n dataset_name='winogrande',config_name='winogrande_xl',\n splits=['train','validation',None]) # test labels are not usable\n \ntasks = [winogrande.load(), codah.load()]) # Aligned datasets (same columns) can be used interchangably \n```\n\n ### Citation and contact\n\nFor more details, refer to this [article:](https://arxiv.org/abs/2301.05948) \n```bib\n@inproceedings{sileo-2024-tasksource,\n title = \"tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework\",\n author = \"Sileo, Damien\",\n booktitle = \"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)\",\n month = may,\n year = \"2024\",\n address = \"Torino, Italia\",\n publisher = \"ELRA and ICCL\",\n url = \"https://aclanthology.org/2024.lrec-main.1361\",\n pages = \"15655--15684\",\n}\n```\nFor help integrating tasksource into your experiments, please contact [damien.sileo@inria.fr](mailto:damien.sileo@inria.fr).\n\n \n" }, { "path": "mtasks.md", "content": "| | id | dataset_name | config_name | task_name | preprocessing_name | task_type |\n|----:|:-------------------------------------------------------------|:--------------------------------------------|:-------------------------------|:------------|:------------------------|:--------------------|\n| 0 | xnli/ru | metaeval/xnli | ru | | xnli | Classification |\n| 1 | xnli/tr | metaeval/xnli | tr | | xnli | Classification |\n| 2 | xnli/ur | metaeval/xnli | ur | | xnli | Classification |\n| 3 | xnli/vi | metaeval/xnli | vi | | xnli | Classification |\n| 4 | xnli/zh | metaeval/xnli | zh | | xnli | Classification |\n| 5 | xnli/hi | metaeval/xnli | hi | | xnli | Classification |\n| 6 | xnli/fr | metaeval/xnli | fr | | xnli | Classification |\n| 7 | xnli/es | metaeval/xnli | es | | xnli | Classification |\n| 8 | xnli/en | metaeval/xnli | en | | xnli | Classification |\n| 9 | xnli/el | metaeval/xnli | el | | xnli | Classification |\n| 10 | xnli/de | metaeval/xnli | de | | xnli | Classification |\n| 11 | xnli/bg | metaeval/xnli | bg | | xnli | Classification |\n| 12 | xnli/ar | metaeval/xnli | ar | | xnli | Classification |\n| 13 | xnli/th | metaeval/xnli | th | | xnli | Classification |\n| 14 | xnli/sw | metaeval/xnli | sw | | xnli | Classification |\n| 15 | americas_nli/all_languages | americas_nli | all_languages | | americas_nli | Classification |\n| 16 | multilingual-NLI-26lang-2mil7/MoritzLaurer--multilingual_nli | MoritzLaurer/multilingual-NLI-26lang-2mil7 | MoritzLaurer--multilingual_nli | | moritz_xnli | Classification |\n| 17 | stsb_multi_mt/en | stsb_multi_mt | en | | stsb_multi_mt | Classification |\n| 18 | stsb_multi_mt/fr | stsb_multi_mt | fr | | stsb_multi_mt | Classification |\n| 19 | stsb_multi_mt/de | stsb_multi_mt | de | | stsb_multi_mt | Classification |\n| 20 | stsb_multi_mt/es | stsb_multi_mt | es | | stsb_multi_mt | Classification |\n| 21 | stsb_multi_mt/it | stsb_multi_mt | it | | stsb_multi_mt | Classification |\n| 22 | stsb_multi_mt/nl | stsb_multi_mt | nl | | stsb_multi_mt | Classification |\n| 23 | stsb_multi_mt/pl | stsb_multi_mt | pl | | stsb_multi_mt | Classification |\n| 24 | stsb_multi_mt/pt | stsb_multi_mt | pt | | stsb_multi_mt | Classification |\n| 25 | stsb_multi_mt/ru | stsb_multi_mt | ru | | stsb_multi_mt | Classification |\n| 26 | stsb_multi_mt/zh | stsb_multi_mt | zh | | stsb_multi_mt | Classification |\n| 27 | paws-x/zh | paws-x | zh | | pawsx | Classification |\n| 28 | paws-x/ja | paws-x | ja | | pawsx | Classification |\n| 29 | paws-x/ko | paws-x | ko | | pawsx | Classification |\n| 30 | paws-x/en | paws-x | en | | pawsx | Classification |\n| 31 | paws-x/de | paws-x | de | | pawsx | Classification |\n| 32 | paws-x/es | paws-x | es | | pawsx | Classification |\n| 33 | paws-x/fr | paws-x | fr | | pawsx | Classification |\n| 34 | miam/vm2 | miam | vm2 | | miam | Classification |\n| 35 | miam/maptask | miam | maptask | | miam | Classification |\n| 36 | miam/loria | miam | loria | | miam | Classification |\n| 37 | miam/dihana | miam | dihana | | miam | Classification |\n| 38 | miam/ilisten | miam | ilisten | | miam | Classification |\n| 39 | x-stance/fr | strombergnlp/x-stance | fr | | xstance | Classification |\n| 40 | x-stance/de | strombergnlp/x-stance | de | | xstance | Classification |\n| 41 | offenseval_2020/da | strombergnlp/offenseval_2020 | da | | offenseval | Classification |\n| 42 | offenseval_2020/tr | strombergnlp/offenseval_2020 | tr | | offenseval | Classification |\n| 43 | offenseval_2020/gr | strombergnlp/offenseval_2020 | gr | | offenseval | Classification |\n| 44 | offenseval_2020/ar | strombergnlp/offenseval_2020 | ar | | offenseval | Classification |\n| 45 | offenseval_dravidian/tamil | offenseval_dravidian | tamil | | offenseval_dravidian | Classification |\n| 46 | offenseval_dravidian/malayalam | offenseval_dravidian | malayalam | | offenseval_dravidian | Classification |\n| 47 | offenseval_dravidian/kannada | offenseval_dravidian | kannada | | offenseval_dravidian | Classification |\n| 48 | MLMA_hate_speech | nedjmaou/MLMA_hate_speech | | | mlma_hate | Classification |\n| 49 | xglue/qam | xglue | qam | | qam | Classification |\n| 50 | x-fact | metaeval/x-fact | | | x_fact | Classification |\n| 51 | xglue/nc | xglue | nc | | xglue___nc | Classification |\n| 52 | xglue/qadsm | xglue | qadsm | | xglue___qadsm | Classification |\n| 53 | xglue/qam | xglue | qam | | xglue___qam | Classification |\n| 54 | xglue/wpr | xglue | wpr | | xglue___wpr | Classification |\n| 55 | xlwic/xlwic_fr_fr | pasinit/xlwic | xlwic_fr_fr | | xlwic | Classification |\n| 56 | xlwic/xlwic_en_ko | pasinit/xlwic | xlwic_en_ko | | xlwic | Classification |\n| 57 | xlwic/xlwic_it_it | pasinit/xlwic | xlwic_it_it | | xlwic | Classification |\n| 58 | xlwic/xlwic_de_de | pasinit/xlwic | xlwic_de_de | | xlwic | Classification |\n| 59 | oasst1_dense_flat/quality | tasksource/oasst1_dense_flat | | quality | oasst1__quality | Classification |\n| 60 | oasst1_dense_flat/toxicity | tasksource/oasst1_dense_flat | | toxicity | oasst1__toxicity | Classification |\n| 61 | oasst1_dense_flat/helpfulness | tasksource/oasst1_dense_flat | | helpfulness | oasst1__helpfulness | Classification |\n| 62 | language-identification | papluca/language-identification | | | language_identification | Classification |\n| 63 | wili_2018 | wili_2018 | | | wili_2018_langid | Classification |\n| 64 | exams/multilingual | exams | multilingual | | exams | MultipleChoice |\n| 65 | xcsr/X-CSQA-ar | xcsr | X-CSQA-ar | | xcsr | MultipleChoice |\n| 66 | xcsr/X-CODAH-zh | xcsr | X-CODAH-zh | | xcsr | MultipleChoice |\n| 67 | xcsr/X-CODAH-de | xcsr | X-CODAH-de | | xcsr | MultipleChoice |\n| 68 | xcsr/X-CSQA-ru | xcsr | X-CSQA-ru | | xcsr | MultipleChoice |\n| 69 | xcsr/X-CODAH-fr | xcsr | X-CODAH-fr | | xcsr | MultipleChoice |\n| 70 | xcsr/X-CODAH-it | xcsr | X-CODAH-it | | xcsr | MultipleChoice |\n| 71 | xcsr/X-CODAH-jap | xcsr | X-CODAH-jap | | xcsr | MultipleChoice |\n| 72 | xcsr/X-CODAH-nl | xcsr | X-CODAH-nl | | xcsr | MultipleChoice |\n| 73 | xcsr/X-CODAH-pt | xcsr | X-CODAH-pt | | xcsr | MultipleChoice |\n| 74 | xcsr/X-CODAH-en | xcsr | X-CODAH-en | | xcsr | MultipleChoice |\n| 75 | xcsr/X-CODAH-ru | xcsr | X-CODAH-ru | | xcsr | MultipleChoice |\n| 76 | xcsr/X-CODAH-ar | xcsr | X-CODAH-ar | | xcsr | MultipleChoice |\n| 77 | xcsr/X-CODAH-vi | xcsr | X-CODAH-vi | | xcsr | MultipleChoice |\n| 78 | xcsr/X-CODAH-hi | xcsr | X-CODAH-hi | | xcsr | MultipleChoice |\n| 79 | xcsr/X-CODAH-sw | xcsr | X-CODAH-sw | | xcsr | MultipleChoice |\n| 80 | xcsr/X-CODAH-ur | xcsr | X-CODAH-ur | | xcsr | MultipleChoice |\n| 81 | xcsr/X-CODAH-pl | xcsr | X-CODAH-pl | | xcsr | MultipleChoice |\n| 82 | xcsr/X-CSQA-ur | xcsr | X-CSQA-ur | | xcsr | MultipleChoice |\n| 83 | xcsr/X-CODAH-es | xcsr | X-CODAH-es | | xcsr | MultipleChoice |\n| 84 | xcsr/X-CSQA-pt | xcsr | X-CSQA-pt | | xcsr | MultipleChoice |\n| 85 | xcsr/X-CSQA-vi | xcsr | X-CSQA-vi | | xcsr | MultipleChoice |\n| 86 | xcsr/X-CSQA-hi | xcsr | X-CSQA-hi | | xcsr | MultipleChoice |\n| 87 | xcsr/X-CSQA-pl | xcsr | X-CSQA-pl | | xcsr | MultipleChoice |\n| 88 | xcsr/X-CSQA-sw | xcsr | X-CSQA-sw | | xcsr | MultipleChoice |\n| 89 | xcsr/X-CSQA-nl | xcsr | X-CSQA-nl | | xcsr | MultipleChoice |\n| 90 | xcsr/X-CSQA-jap | xcsr | X-CSQA-jap | | xcsr | MultipleChoice |\n| 91 | xcsr/X-CSQA-it | xcsr | X-CSQA-it | | xcsr | MultipleChoice |\n| 92 | xcsr/X-CSQA-es | xcsr | X-CSQA-es | | xcsr | MultipleChoice |\n| 93 | xcsr/X-CSQA-fr | xcsr | X-CSQA-fr | | xcsr | MultipleChoice |\n| 94 | xcsr/X-CSQA-zh | xcsr | X-CSQA-zh | | xcsr | MultipleChoice |\n| 95 | xcsr/X-CSQA-en | xcsr | X-CSQA-en | | xcsr | MultipleChoice |\n| 96 | xcsr/X-CSQA-de | xcsr | X-CSQA-de | | xcsr | MultipleChoice |\n| 97 | xcopa/qu | xcopa | qu | | xcopa | MultipleChoice |\n| 98 | xcopa/it | xcopa | it | | xcopa | MultipleChoice |\n| 99 | xcopa/ht | xcopa | ht | | xcopa | MultipleChoice |\n| 100 | xcopa/et | xcopa | et | | xcopa | MultipleChoice |\n| 101 | xcopa/vi | xcopa | vi | | xcopa | MultipleChoice |\n| 102 | xcopa/id | xcopa | id | | xcopa | MultipleChoice |\n| 103 | xcopa/translation-et | xcopa | translation-et | | xcopa | MultipleChoice |\n| 104 | xcopa/th | xcopa | th | | xcopa | MultipleChoice |\n| 105 | xcopa/sw | xcopa | sw | | xcopa | MultipleChoice |\n| 106 | xcopa/translation-sw | xcopa | translation-sw | | xcopa | MultipleChoice |\n| 107 | xcopa/translation-ht | xcopa | translation-ht | | xcopa | MultipleChoice |\n| 108 | xcopa/translation-it | xcopa | translation-it | | xcopa | MultipleChoice |\n| 109 | xcopa/ta | xcopa | ta | | xcopa | MultipleChoice |\n| 110 | xcopa/translation-zh | xcopa | translation-zh | | xcopa | MultipleChoice |\n| 111 | xcopa/translation-vi | xcopa | translation-vi | | xcopa | MultipleChoice |\n| 112 | xcopa/translation-id | xcopa | translation-id | | xcopa | MultipleChoice |\n| 113 | xcopa/translation-tr | xcopa | translation-tr | | xcopa | MultipleChoice |\n| 114 | xcopa/translation-th | xcopa | translation-th | | xcopa | MultipleChoice |\n| 115 | xcopa/translation-ta | xcopa | translation-ta | | xcopa | MultipleChoice |\n| 116 | xcopa/zh | xcopa | zh | | xcopa | MultipleChoice |\n| 117 | xcopa/tr | xcopa | tr | | xcopa | MultipleChoice |\n| 118 | xstory_cloze/eu | juletxara/xstory_cloze | eu | | xstory | MultipleChoice |\n| 119 | xstory_cloze/my | juletxara/xstory_cloze | my | | xstory | MultipleChoice |\n| 120 | xstory_cloze/te | juletxara/xstory_cloze | te | | xstory | MultipleChoice |\n| 121 | xstory_cloze/sw | juletxara/xstory_cloze | sw | | xstory | MultipleChoice |\n| 122 | xstory_cloze/en | juletxara/xstory_cloze | en | | xstory | MultipleChoice |\n| 123 | xstory_cloze/ru | juletxara/xstory_cloze | ru | | xstory | MultipleChoice |\n| 124 | xstory_cloze/zh | juletxara/xstory_cloze | zh | | xstory | MultipleChoice |\n| 125 | xstory_cloze/es | juletxara/xstory_cloze | es | | xstory | MultipleChoice |\n| 126 | xstory_cloze/ar | juletxara/xstory_cloze | ar | | xstory | MultipleChoice |\n| 127 | xstory_cloze/hi | juletxara/xstory_cloze | hi | | xstory | MultipleChoice |\n| 128 | xstory_cloze/id | juletxara/xstory_cloze | id | | xstory | MultipleChoice |\n| 129 | xglue/ner | xglue | ner | | xglue_ner | TokenClassification |\n| 130 | xglue/pos | xglue | pos | | xglue_pos | TokenClassification |\n| 131 | universal_dependencies/nyq_aha/pos | universal_dependencies | nyq_aha | pos | udep__pos | TokenClassification |\n| 132 | universal_dependencies/sme_giella/pos | universal_dependencies | sme_giella | pos | udep__pos | TokenClassification |\n| 133 | universal_dependencies/no_bokmaal/pos | universal_dependencies | no_bokmaal | pos | udep__pos | TokenClassification |\n| 134 | universal_dependencies/no_nynorsk/pos | universal_dependencies | no_nynorsk | pos | udep__pos | TokenClassification |\n| 135 | universal_dependencies/no_nynorsklia/pos | universal_dependencies | no_nynorsklia | pos | udep__pos | TokenClassification |\n| 136 | universal_dependencies/cu_proiel/pos | universal_dependencies | cu_proiel | pos | udep__pos | TokenClassification |\n| 137 | universal_dependencies/fro_srcmf/pos | universal_dependencies | fro_srcmf | pos | udep__pos | TokenClassification |\n| 138 | universal_dependencies/orv_rnc/pos | universal_dependencies | orv_rnc | pos | udep__pos | TokenClassification |\n| 139 | universal_dependencies/pl_lfg/pos | universal_dependencies | pl_lfg | pos | udep__pos | TokenClassification |\n| 140 | universal_dependencies/otk_tonqq/pos | universal_dependencies | otk_tonqq | pos | udep__pos | TokenClassification |\n| 141 | universal_dependencies/fa_perdt/pos | universal_dependencies | fa_perdt | pos | udep__pos | TokenClassification |\n| 142 | universal_dependencies/fa_seraji/pos | universal_dependencies | fa_seraji | pos | udep__pos | TokenClassification |\n| 143 | universal_dependencies/pcm_nsc/pos | universal_dependencies | pcm_nsc | pos | udep__pos | TokenClassification |\n| 144 | universal_dependencies/pl_pdb/pos | universal_dependencies | pl_pdb | pos | udep__pos | TokenClassification |\n| 145 | universal_dependencies/pl_pud/pos | universal_dependencies | pl_pud | pos | udep__pos | TokenClassification |\n| 146 | universal_dependencies/pt_bosque/pos | universal_dependencies | pt_bosque | pos | udep__pos | TokenClassification |\n| 147 | universal_dependencies/pt_gsd/pos | universal_dependencies | pt_gsd | pos | udep__pos | TokenClassification |\n| 148 | universal_dependencies/pt_pud/pos | universal_dependencies | pt_pud | pos | udep__pos | TokenClassification |\n| 149 | universal_dependencies/orv_torot/pos | universal_dependencies | orv_torot | pos | udep__pos | TokenClassification |\n| 150 | universal_dependencies/myu_tudet/pos | universal_dependencies | myu_tudet | pos | udep__pos | TokenClassification |\n| 151 | universal_dependencies/gv_cadhan/pos | universal_dependencies | gv_cadhan | pos | udep__pos | TokenClassification |\n| 152 | universal_dependencies/gun_thomas/pos | universal_dependencies | gun_thomas | pos | udep__pos | TokenClassification |\n| 153 | universal_dependencies/koi_uh/pos | universal_dependencies | koi_uh | pos | udep__pos | TokenClassification |\n| 154 | universal_dependencies/kpv_ikdp/pos | universal_dependencies | kpv_ikdp | pos | udep__pos | TokenClassification |\n| 155 | universal_dependencies/kpv_lattice/pos | universal_dependencies | kpv_lattice | pos | udep__pos | TokenClassification |\n| 156 | universal_dependencies/ko_gsd/pos | universal_dependencies | ko_gsd | pos | udep__pos | TokenClassification |\n| 157 | universal_dependencies/ko_kaist/pos | universal_dependencies | ko_kaist | pos | udep__pos | TokenClassification |\n| 158 | universal_dependencies/ko_pud/pos | universal_dependencies | ko_pud | pos | udep__pos | TokenClassification |\n| 159 | universal_dependencies/kmr_mg/pos | universal_dependencies | kmr_mg | pos | udep__pos | TokenClassification |\n| 160 | universal_dependencies/la_ittb/pos | universal_dependencies | la_ittb | pos | udep__pos | TokenClassification |\n| 161 | universal_dependencies/la_llct/pos | universal_dependencies | la_llct | pos | udep__pos | TokenClassification |\n| 162 | universal_dependencies/la_perseus/pos | universal_dependencies | la_perseus | pos | udep__pos | TokenClassification |\n| 163 | universal_dependencies/la_proiel/pos | universal_dependencies | la_proiel | pos | udep__pos | TokenClassification |\n| 164 | universal_dependencies/lv_lvtb/pos | universal_dependencies | lv_lvtb | pos | udep__pos | TokenClassification |\n| 165 | universal_dependencies/lt_alksnis/pos | universal_dependencies | lt_alksnis | pos | udep__pos | TokenClassification |\n| 166 | universal_dependencies/lt_hse/pos | universal_dependencies | lt_hse | pos | udep__pos | TokenClassification |\n| 167 | universal_dependencies/olo_kkpp/pos | universal_dependencies | olo_kkpp | pos | udep__pos | TokenClassification |\n| 168 | universal_dependencies/mt_mudt/pos | universal_dependencies | mt_mudt | pos | udep__pos | TokenClassification |\n| 169 | universal_dependencies/ro_nonstandard/pos | universal_dependencies | ro_nonstandard | pos | udep__pos | TokenClassification |\n| 170 | universal_dependencies/mr_ufal/pos | universal_dependencies | mr_ufal | pos | udep__pos | TokenClassification |\n| 171 | universal_dependencies/gun_dooley/pos | universal_dependencies | gun_dooley | pos | udep__pos | TokenClassification |\n| 172 | universal_dependencies/mdf_jr/pos | universal_dependencies | mdf_jr | pos | udep__pos | TokenClassification |\n| 173 | universal_dependencies/ro_rrt/pos | universal_dependencies | ro_rrt | pos | udep__pos | TokenClassification |\n| 174 | universal_dependencies/ru_taiga/pos | universal_dependencies | ru_taiga | pos | udep__pos | TokenClassification |\n| 175 | universal_dependencies/ru_gsd/pos | universal_dependencies | ru_gsd | pos | udep__pos | TokenClassification |\n| 176 | universal_dependencies/ta_mwtt/pos | universal_dependencies | ta_mwtt | pos | udep__pos | TokenClassification |\n| 177 | universal_dependencies/ta_ttb/pos | universal_dependencies | ta_ttb | pos | udep__pos | TokenClassification |\n| 178 | universal_dependencies/te_mtg/pos | universal_dependencies | te_mtg | pos | udep__pos | TokenClassification |\n| 179 | universal_dependencies/th_pud/pos | universal_dependencies | th_pud | pos | udep__pos | TokenClassification |\n| 180 | universal_dependencies/tpn_tudet/pos | universal_dependencies | tpn_tudet | pos | udep__pos | TokenClassification |\n| 181 | universal_dependencies/qtd_sagt/pos | universal_dependencies | qtd_sagt | pos | udep__pos | TokenClassification |\n| 182 | universal_dependencies/tr_boun/pos | universal_dependencies | tr_boun | pos | udep__pos | TokenClassification |\n| 183 | universal_dependencies/tr_gb/pos | universal_dependencies | tr_gb | pos | udep__pos | TokenClassification |\n| 184 | universal_dependencies/tr_imst/pos | universal_dependencies | tr_imst | pos | udep__pos | TokenClassification |\n| 185 | universal_dependencies/tr_pud/pos | universal_dependencies | tr_pud | pos | udep__pos | TokenClassification |\n| 186 | universal_dependencies/uk_iu/pos | universal_dependencies | uk_iu | pos | udep__pos | TokenClassification |\n| 187 | universal_dependencies/hsb_ufal/pos | universal_dependencies | hsb_ufal | pos | udep__pos | TokenClassification |\n| 188 | universal_dependencies/ur_udtb/pos | universal_dependencies | ur_udtb | pos | udep__pos | TokenClassification |\n| 189 | universal_dependencies/ug_udt/pos | universal_dependencies | ug_udt | pos | udep__pos | TokenClassification |\n| 190 | universal_dependencies/vi_vtb/pos | universal_dependencies | vi_vtb | pos | udep__pos | TokenClassification |\n| 191 | universal_dependencies/wbp_ufal/pos | universal_dependencies | wbp_ufal | pos | udep__pos | TokenClassification |\n| 192 | universal_dependencies/cy_ccg/pos | universal_dependencies | cy_ccg | pos | udep__pos | TokenClassification |\n| 193 | universal_dependencies/wo_wtb/pos | universal_dependencies | wo_wtb | pos | udep__pos | TokenClassification |\n| 194 | universal_dependencies/yo_ytb/pos | universal_dependencies | yo_ytb | pos | udep__pos | TokenClassification |\n| 195 | universal_dependencies/tl_ugnayan/pos | universal_dependencies | tl_ugnayan | pos | udep__pos | TokenClassification |\n| 196 | universal_dependencies/ro_simonero/pos | universal_dependencies | ro_simonero | pos | udep__pos | TokenClassification |\n| 197 | universal_dependencies/tl_trg/pos | universal_dependencies | tl_trg | pos | udep__pos | TokenClassification |\n| 198 | universal_dependencies/sv_talbanken/pos | universal_dependencies | sv_talbanken | pos | udep__pos | TokenClassification |\n| 199 | universal_dependencies/ru_pud/pos | universal_dependencies | ru_pud | pos | udep__pos | TokenClassification |\n| 200 | universal_dependencies/ru_syntagrus/pos | universal_dependencies | ru_syntagrus | pos | udep__pos | TokenClassification |\n| 201 | universal_dependencies/kfm_aha/pos | universal_dependencies | kfm_aha | pos | udep__pos | TokenClassification |\n| 202 | universal_dependencies/sa_ufal/pos | universal_dependencies | sa_ufal | pos | udep__pos | TokenClassification |\n| 203 | universal_dependencies/sa_vedic/pos | universal_dependencies | sa_vedic | pos | udep__pos | TokenClassification |\n| 204 | universal_dependencies/gd_arcosg/pos | universal_dependencies | gd_arcosg | pos | udep__pos | TokenClassification |\n| 205 | universal_dependencies/sr_set/pos | universal_dependencies | sr_set | pos | udep__pos | TokenClassification |\n| 206 | universal_dependencies/sms_giellagas/pos | universal_dependencies | sms_giellagas | pos | udep__pos | TokenClassification |\n| 207 | universal_dependencies/sk_snk/pos | universal_dependencies | sk_snk | pos | udep__pos | TokenClassification |\n| 208 | universal_dependencies/sl_ssj/pos | universal_dependencies | sl_ssj | pos | udep__pos | TokenClassification |\n| 209 | universal_dependencies/sl_sst/pos | universal_dependencies | sl_sst | pos | udep__pos | TokenClassification |\n| 210 | universal_dependencies/soj_aha/pos | universal_dependencies | soj_aha | pos | udep__pos | TokenClassification |\n| 211 | universal_dependencies/ajp_madar/pos | universal_dependencies | ajp_madar | pos | udep__pos | TokenClassification |\n| 212 | universal_dependencies/es_ancora/pos | universal_dependencies | es_ancora | pos | udep__pos | TokenClassification |\n| 213 | universal_dependencies/es_gsd/pos | universal_dependencies | es_gsd | pos | udep__pos | TokenClassification |\n| 214 | universal_dependencies/es_pud/pos | universal_dependencies | es_pud | pos | udep__pos | TokenClassification |\n| 215 | universal_dependencies/swl_sslc/pos | universal_dependencies | swl_sslc | pos | udep__pos | TokenClassification |\n| 216 | universal_dependencies/sv_lines/pos | universal_dependencies | sv_lines | pos | udep__pos | TokenClassification |\n| 217 | universal_dependencies/sv_pud/pos | universal_dependencies | sv_pud | pos | udep__pos | TokenClassification |\n| 218 | universal_dependencies/gsw_uzh/pos | universal_dependencies | gsw_uzh | pos | udep__pos | TokenClassification |\n| 219 | universal_dependencies/kk_ktb/pos | universal_dependencies | kk_ktb | pos | udep__pos | TokenClassification |\n| 220 | universal_dependencies/hi_hdtb/pos | universal_dependencies | hi_hdtb | pos | udep__pos | TokenClassification |\n| 221 | universal_dependencies/ja_pud/pos | universal_dependencies | ja_pud | pos | udep__pos | TokenClassification |\n| 222 | universal_dependencies/zh_gsd/pos | universal_dependencies | zh_gsd | pos | udep__pos | TokenClassification |\n| 223 | universal_dependencies/zh_gsdsimp/pos | universal_dependencies | zh_gsdsimp | pos | udep__pos | TokenClassification |\n| 224 | universal_dependencies/zh_hk/pos | universal_dependencies | zh_hk | pos | udep__pos | TokenClassification |\n| 225 | universal_dependencies/zh_pud/pos | universal_dependencies | zh_pud | pos | udep__pos | TokenClassification |\n| 226 | universal_dependencies/ckt_hse/pos | universal_dependencies | ckt_hse | pos | udep__pos | TokenClassification |\n| 227 | universal_dependencies/lzh_kyoto/pos | universal_dependencies | lzh_kyoto | pos | udep__pos | TokenClassification |\n| 228 | universal_dependencies/cop_scriptorium/pos | universal_dependencies | cop_scriptorium | pos | udep__pos | TokenClassification |\n| 229 | universal_dependencies/hr_set/pos | universal_dependencies | hr_set | pos | udep__pos | TokenClassification |\n| 230 | universal_dependencies/cs_cac/pos | universal_dependencies | cs_cac | pos | udep__pos | TokenClassification |\n| 231 | universal_dependencies/cs_cltt/pos | universal_dependencies | cs_cltt | pos | udep__pos | TokenClassification |\n| 232 | universal_dependencies/cs_fictree/pos | universal_dependencies | cs_fictree | pos | udep__pos | TokenClassification |\n| 233 | universal_dependencies/cs_pdt/pos | universal_dependencies | cs_pdt | pos | udep__pos | TokenClassification |\n| 234 | universal_dependencies/cs_pud/pos | universal_dependencies | cs_pud | pos | udep__pos | TokenClassification |\n| 235 | universal_dependencies/da_ddt/pos | universal_dependencies | da_ddt | pos | udep__pos | TokenClassification |\n| 236 | universal_dependencies/nl_alpino/pos | universal_dependencies | nl_alpino | pos | udep__pos | TokenClassification |\n| 237 | universal_dependencies/nl_lassysmall/pos | universal_dependencies | nl_lassysmall | pos | udep__pos | TokenClassification |\n| 238 | universal_dependencies/en_esl/pos | universal_dependencies | en_esl | pos | udep__pos | TokenClassification |\n| 239 | universal_dependencies/en_ewt/pos | universal_dependencies | en_ewt | pos | udep__pos | TokenClassification |\n| 240 | universal_dependencies/en_gum/pos | universal_dependencies | en_gum | pos | udep__pos | TokenClassification |\n| 241 | universal_dependencies/zh_cfl/pos | universal_dependencies | zh_cfl | pos | udep__pos | TokenClassification |\n| 242 | universal_dependencies/ca_ancora/pos | universal_dependencies | ca_ancora | pos | udep__pos | TokenClassification |\n| 243 | universal_dependencies/yue_hk/pos | universal_dependencies | yue_hk | pos | udep__pos | TokenClassification |\n| 244 | universal_dependencies/bxr_bdt/pos | universal_dependencies | bxr_bdt | pos | udep__pos | TokenClassification |\n| 245 | universal_dependencies/af_afribooms/pos | universal_dependencies | af_afribooms | pos | udep__pos | TokenClassification |\n| 246 | universal_dependencies/krl_kkpp/pos | universal_dependencies | krl_kkpp | pos | udep__pos | TokenClassification |\n| 247 | universal_dependencies/akk_riao/pos | universal_dependencies | akk_riao | pos | udep__pos | TokenClassification |\n| 248 | universal_dependencies/aqz_tudet/pos | universal_dependencies | aqz_tudet | pos | udep__pos | TokenClassification |\n| 249 | universal_dependencies/sq_tsa/pos | universal_dependencies | sq_tsa | pos | udep__pos | TokenClassification |\n| 250 | universal_dependencies/am_att/pos | universal_dependencies | am_att | pos | udep__pos | TokenClassification |\n| 251 | universal_dependencies/grc_perseus/pos | universal_dependencies | grc_perseus | pos | udep__pos | TokenClassification |\n| 252 | universal_dependencies/grc_proiel/pos | universal_dependencies | grc_proiel | pos | udep__pos | TokenClassification |\n| 253 | universal_dependencies/apu_ufpa/pos | universal_dependencies | apu_ufpa | pos | udep__pos | TokenClassification |\n| 254 | universal_dependencies/en_gumreddit/pos | universal_dependencies | en_gumreddit | pos | udep__pos | TokenClassification |\n| 255 | universal_dependencies/ar_nyuad/pos | universal_dependencies | ar_nyuad | pos | udep__pos | TokenClassification |\n| 256 | universal_dependencies/ar_pud/pos | universal_dependencies | ar_pud | pos | udep__pos | TokenClassification |\n| 257 | universal_dependencies/hy_armtdp/pos | universal_dependencies | hy_armtdp | pos | udep__pos | TokenClassification |\n| 258 | universal_dependencies/aii_as/pos | universal_dependencies | aii_as | pos | udep__pos | TokenClassification |\n| 259 | universal_dependencies/bm_crb/pos | universal_dependencies | bm_crb | pos | udep__pos | TokenClassification |\n| 260 | universal_dependencies/eu_bdt/pos | universal_dependencies | eu_bdt | pos | udep__pos | TokenClassification |\n| 261 | universal_dependencies/be_hse/pos | universal_dependencies | be_hse | pos | udep__pos | TokenClassification |\n| 262 | universal_dependencies/bho_bhtb/pos | universal_dependencies | bho_bhtb | pos | udep__pos | TokenClassification |\n| 263 | universal_dependencies/br_keb/pos | universal_dependencies | br_keb | pos | udep__pos | TokenClassification |\n| 264 | universal_dependencies/bg_btb/pos | universal_dependencies | bg_btb | pos | udep__pos | TokenClassification |\n| 265 | universal_dependencies/ar_padt/pos | universal_dependencies | ar_padt | pos | udep__pos | TokenClassification |\n| 266 | universal_dependencies/en_lines/pos | universal_dependencies | en_lines | pos | udep__pos | TokenClassification |\n| 267 | universal_dependencies/akk_pisandub/pos | universal_dependencies | akk_pisandub | pos | udep__pos | TokenClassification |\n| 268 | universal_dependencies/en_pronouns/pos | universal_dependencies | en_pronouns | pos | udep__pos | TokenClassification |\n| 269 | universal_dependencies/el_gdt/pos | universal_dependencies | el_gdt | pos | udep__pos | TokenClassification |\n| 270 | universal_dependencies/he_htb/pos | universal_dependencies | he_htb | pos | udep__pos | TokenClassification |\n| 271 | universal_dependencies/qhe_hiencs/pos | universal_dependencies | qhe_hiencs | pos | udep__pos | TokenClassification |\n| 272 | universal_dependencies/hi_pud/pos | universal_dependencies | hi_pud | pos | udep__pos | TokenClassification |\n| 273 | universal_dependencies/hu_szeged/pos | universal_dependencies | hu_szeged | pos | udep__pos | TokenClassification |\n| 274 | universal_dependencies/is_icepahc/pos | universal_dependencies | is_icepahc | pos | udep__pos | TokenClassification |\n| 275 | universal_dependencies/id_csui/pos | universal_dependencies | id_csui | pos | udep__pos | TokenClassification |\n| 276 | universal_dependencies/id_gsd/pos | universal_dependencies | id_gsd | pos | udep__pos | TokenClassification |\n| 277 | universal_dependencies/id_pud/pos | universal_dependencies | id_pud | pos | udep__pos | TokenClassification |\n| 278 | universal_dependencies/ga_idt/pos | universal_dependencies | ga_idt | pos | udep__pos | TokenClassification |\n| 279 | universal_dependencies/it_isdt/pos | universal_dependencies | it_isdt | pos | udep__pos | TokenClassification |\n| 280 | universal_dependencies/it_partut/pos | universal_dependencies | it_partut | pos | udep__pos | TokenClassification |\n| 281 | universal_dependencies/it_postwita/pos | universal_dependencies | it_postwita | pos | udep__pos | TokenClassification |\n| 282 | universal_dependencies/it_pud/pos | universal_dependencies | it_pud | pos | udep__pos | TokenClassification |\n| 283 | universal_dependencies/it_twittiro/pos | universal_dependencies | it_twittiro | pos | udep__pos | TokenClassification |\n| 284 | universal_dependencies/it_vit/pos | universal_dependencies | it_vit | pos | udep__pos | TokenClassification |\n| 285 | universal_dependencies/ja_bccwj/pos | universal_dependencies | ja_bccwj | pos | udep__pos | TokenClassification |\n| 286 | universal_dependencies/ja_gsd/pos | universal_dependencies | ja_gsd | pos | udep__pos | TokenClassification |\n| 287 | universal_dependencies/ja_modern/pos | universal_dependencies | ja_modern | pos | udep__pos | TokenClassification |\n| 288 | universal_dependencies/got_proiel/pos | universal_dependencies | got_proiel | pos | udep__pos | TokenClassification |\n| 289 | universal_dependencies/de_pud/pos | universal_dependencies | de_pud | pos | udep__pos | TokenClassification |\n| 290 | universal_dependencies/is_pud/pos | universal_dependencies | is_pud | pos | udep__pos | TokenClassification |\n| 291 | universal_dependencies/de_hdt/pos | universal_dependencies | de_hdt | pos | udep__pos | TokenClassification |\n| 292 | universal_dependencies/en_pud/pos | universal_dependencies | en_pud | pos | udep__pos | TokenClassification |\n| 293 | universal_dependencies/myv_jr/pos | universal_dependencies | myv_jr | pos | udep__pos | TokenClassification |\n| 294 | universal_dependencies/de_lit/pos | universal_dependencies | de_lit | pos | udep__pos | TokenClassification |\n| 295 | universal_dependencies/et_ewt/pos | universal_dependencies | et_ewt | pos | udep__pos | TokenClassification |\n| 296 | universal_dependencies/fo_farpahc/pos | universal_dependencies | fo_farpahc | pos | udep__pos | TokenClassification |\n| 297 | universal_dependencies/fo_oft/pos | universal_dependencies | fo_oft | pos | udep__pos | TokenClassification |\n| 298 | universal_dependencies/fi_ftb/pos | universal_dependencies | fi_ftb | pos | udep__pos | TokenClassification |\n| 299 | universal_dependencies/fi_ood/pos | universal_dependencies | fi_ood | pos | udep__pos | TokenClassification |\n| 300 | universal_dependencies/fi_pud/pos | universal_dependencies | fi_pud | pos | udep__pos | TokenClassification |\n| 301 | universal_dependencies/fi_tdt/pos | universal_dependencies | fi_tdt | pos | udep__pos | TokenClassification |\n| 302 | universal_dependencies/et_edt/pos | universal_dependencies | et_edt | pos | udep__pos | TokenClassification |\n| 303 | universal_dependencies/fr_ftb/pos | universal_dependencies | fr_ftb | pos | udep__pos | TokenClassification |\n| 304 | universal_dependencies/fr_fqb/pos | universal_dependencies | fr_fqb | pos | udep__pos | TokenClassification |\n| 305 | universal_dependencies/de_gsd/pos | universal_dependencies | de_gsd | pos | udep__pos | TokenClassification |\n| 306 | universal_dependencies/gl_treegal/pos | universal_dependencies | gl_treegal | pos | udep__pos | TokenClassification |\n| 307 | universal_dependencies/gl_ctg/pos | universal_dependencies | gl_ctg | pos | udep__pos | TokenClassification |\n| 308 | universal_dependencies/fr_spoken/pos | universal_dependencies | fr_spoken | pos | udep__pos | TokenClassification |\n| 309 | universal_dependencies/en_partut/pos | universal_dependencies | en_partut | pos | udep__pos | TokenClassification |\n| 310 | universal_dependencies/fr_pud/pos | universal_dependencies | fr_pud | pos | udep__pos | TokenClassification |\n| 311 | universal_dependencies/fr_partut/pos | universal_dependencies | fr_partut | pos | udep__pos | TokenClassification |\n| 312 | universal_dependencies/fr_sequoia/pos | universal_dependencies | fr_sequoia | pos | udep__pos | TokenClassification |\n| 313 | universal_dependencies/fr_gsd/pos | universal_dependencies | fr_gsd | pos | udep__pos | TokenClassification |\n| 314 | oasst1_pairwise_rlhf_reward | tasksource/oasst1_pairwise_rlhf_reward | | | oasst_rlhf | MultipleChoice |\n| 315 | multilingual-sentiments/all | tyqiangz/multilingual-sentiments | all | | sentiment | Classification |\n| 316 | tweet_sentiment_multilingual/arabic | cardiffnlp/tweet_sentiment_multilingual | arabic | | tweet_sentiment | Classification |\n| 317 | tweet_sentiment_multilingual/french | cardiffnlp/tweet_sentiment_multilingual | french | | tweet_sentiment | Classification |\n| 318 | tweet_sentiment_multilingual/english | cardiffnlp/tweet_sentiment_multilingual | english | | tweet_sentiment | Classification |\n| 319 | tweet_sentiment_multilingual/hindi | cardiffnlp/tweet_sentiment_multilingual | hindi | | tweet_sentiment | Classification |\n| 320 | tweet_sentiment_multilingual/portuguese | cardiffnlp/tweet_sentiment_multilingual | portuguese | | tweet_sentiment | Classification |\n| 321 | tweet_sentiment_multilingual/spanish | cardiffnlp/tweet_sentiment_multilingual | spanish | | tweet_sentiment | Classification |\n| 322 | tweet_sentiment_multilingual/all | cardiffnlp/tweet_sentiment_multilingual | all | | tweet_sentiment | Classification |\n| 323 | tweet_sentiment_multilingual/german | cardiffnlp/tweet_sentiment_multilingual | german | | tweet_sentiment | Classification |\n| 324 | tweet_sentiment_multilingual/italian | cardiffnlp/tweet_sentiment_multilingual | italian | | tweet_sentiment | Classification |\n| 325 | amazon_reviews_multi/all_languages | amazon_reviews_multi | all_languages | | review_sentiment | Classification |\n| 326 | universal-joy | metaeval/universal-joy | | | emotion | Classification |\n| 327 | mms | Brand24/mms | | | mms_sentiment | Classification |\n| 328 | mapa | joelito/mapa | | | mapa_fine | TokenClassification |\n| 329 | mapa | joelito/mapa | | | mapa_corase | TokenClassification |\n| 330 | ACES | nikitam/ACES | | | aces_ranking | MultipleChoice |\n| 331 | ACES | nikitam/ACES | | | aces_phenomena | Classification |\n| 332 | massive/my-MM | AmazonScience/massive | my-MM | | amazon_intent | Classification |\n| 333 | massive/ro-RO | AmazonScience/massive | ro-RO | | amazon_intent | Classification |\n| 334 | massive/pt-PT | AmazonScience/massive | pt-PT | | amazon_intent | Classification |\n| 335 | massive/pl-PL | AmazonScience/massive | pl-PL | | amazon_intent | Classification |\n| 336 | massive/nl-NL | AmazonScience/massive | nl-NL | | amazon_intent | Classification |\n| 337 | massive/nb-NO | AmazonScience/massive | nb-NO | | amazon_intent | Classification |\n| 338 | massive/es-ES | AmazonScience/massive | es-ES | | amazon_intent | Classification |\n| 339 | massive/ms-MY | AmazonScience/massive | ms-MY | | amazon_intent | Classification |\n| 340 | massive/mn-MN | AmazonScience/massive | mn-MN | | amazon_intent | Classification |\n| 341 | massive/ml-IN | AmazonScience/massive | ml-IN | | amazon_intent | Classification |\n| 342 | massive/lv-LV | AmazonScience/massive | lv-LV | | amazon_intent | Classification |\n| 343 | massive/ko-KR | AmazonScience/massive | ko-KR | | amazon_intent | Classification |\n| 344 | massive/ru-RU | AmazonScience/massive | ru-RU | | amazon_intent | Classification |\n| 345 | massive/kn-IN | AmazonScience/massive | kn-IN | | amazon_intent | Classification |\n| 346 | massive/ka-GE | AmazonScience/massive | ka-GE | | amazon_intent | Classification |\n| 347 | massive/jv-ID | AmazonScience/massive | jv-ID | | amazon_intent | Classification |\n| 348 | massive/ja-JP | AmazonScience/massive | ja-JP | | amazon_intent | Classification |\n| 349 | massive/it-IT | AmazonScience/massive | it-IT | | amazon_intent | Classification |\n| 350 | massive/is-IS | AmazonScience/massive | is-IS | | amazon_intent | Classification |\n| 351 | massive/id-ID | AmazonScience/massive | id-ID | | amazon_intent | Classification |\n| 352 | massive/hy-AM | AmazonScience/massive | hy-AM | | amazon_intent | Classification |\n| 353 | massive/hu-HU | AmazonScience/massive | hu-HU | | amazon_intent | Classification |\n| 354 | massive/hi-IN | AmazonScience/massive | hi-IN | | amazon_intent | Classification |\n| 355 | massive/he-IL | AmazonScience/massive | he-IL | | amazon_intent | Classification |\n| 356 | massive/fr-FR | AmazonScience/massive | fr-FR | | amazon_intent | Classification |\n| 357 | massive/km-KH | AmazonScience/massive | km-KH | | amazon_intent | Classification |\n| 358 | massive/fi-FI | AmazonScience/massive | fi-FI | | amazon_intent | Classification |\n| 359 | massive/sl-SL | AmazonScience/massive | sl-SL | | amazon_intent | Classification |\n| 360 | massive/sv-SE | AmazonScience/massive | sv-SE | | amazon_intent | Classification |\n| 361 | massive/af-ZA | AmazonScience/massive | af-ZA | | amazon_intent | Classification |\n| 362 | massive/am-ET | AmazonScience/massive | am-ET | | amazon_intent | Classification |\n| 363 | massive/ar-SA | AmazonScience/massive | ar-SA | | amazon_intent | Classification |\n| 364 | massive/az-AZ | AmazonScience/massive | az-AZ | | amazon_intent | Classification |\n| 365 | massive/bn-BD | AmazonScience/massive | bn-BD | | amazon_intent | Classification |\n| 366 | massive/ca-ES | AmazonScience/massive | ca-ES | | amazon_intent | Classification |\n| 367 | massive/cy-GB | AmazonScience/massive | cy-GB | | amazon_intent | Classification |\n| 368 | massive/da-DK | AmazonScience/massive | da-DK | | amazon_intent | Classification |\n| 369 | massive/de-DE | AmazonScience/massive | de-DE | | amazon_intent | Classification |\n| 370 | massive/el-GR | AmazonScience/massive | el-GR | | amazon_intent | Classification |\n| 371 | massive/sq-AL | AmazonScience/massive | sq-AL | | amazon_intent | Classification |\n| 372 | massive/en-US | AmazonScience/massive | en-US | | amazon_intent | Classification |\n| 373 | massive/all | AmazonScience/massive | all | | amazon_intent | Classification |\n| 374 | massive/zh-TW | AmazonScience/massive | zh-TW | | amazon_intent | Classification |\n| 375 | massive/zh-CN | AmazonScience/massive | zh-CN | | amazon_intent | Classification |\n| 376 | massive/vi-VN | AmazonScience/massive | vi-VN | | amazon_intent | Classification |\n| 377 | massive/ur-PK | AmazonScience/massive | ur-PK | | amazon_intent | Classification |\n| 378 | massive/tr-TR | AmazonScience/massive | tr-TR | | amazon_intent | Classification |\n| 379 | massive/tl-PH | AmazonScience/massive | tl-PH | | amazon_intent | Classification |\n| 380 | massive/th-TH | AmazonScience/massive | th-TH | | amazon_intent | Classification |\n| 381 | massive/te-IN | AmazonScience/massive | te-IN | | amazon_intent | Classification |\n| 382 | massive/ta-IN | AmazonScience/massive | ta-IN | | amazon_intent | Classification |\n| 383 | massive/sw-KE | AmazonScience/massive | sw-KE | | amazon_intent | Classification |\n| 384 | massive/all_1.1 | AmazonScience/massive | all_1.1 | | amazon_intent | Classification |\n| 385 | massive/fa-IR | AmazonScience/massive | fa-IR | | amazon_intent | Classification |\n| 386 | tydi-as2-balanced | tasksource/tydi-as2-balanced | | | tidy_as2 | Classification |\n| 387 | multiconer_v2/Hindi (HI) | MultiCoNER/multiconer_v2 | Hindi (HI) | | multiconer | TokenClassification |\n| 388 | multiconer_v2/Multilingual (MULTI) | MultiCoNER/multiconer_v2 | Multilingual (MULTI) | | multiconer | TokenClassification |\n| 389 | multiconer_v2/Ukrainian (UK) | MultiCoNER/multiconer_v2 | Ukrainian (UK) | | multiconer | TokenClassification |\n| 390 | multiconer_v2/Swedish (SV) | MultiCoNER/multiconer_v2 | Swedish (SV) | | multiconer | TokenClassification |\n| 391 | multiconer_v2/Spanish (ES) | MultiCoNER/multiconer_v2 | Spanish (ES) | | multiconer | TokenClassification |\n| 392 | multiconer_v2/Bangla (BN) | MultiCoNER/multiconer_v2 | Bangla (BN) | | multiconer | TokenClassification |\n| 393 | multiconer_v2/Chinese (ZH) | MultiCoNER/multiconer_v2 | Chinese (ZH) | | multiconer | TokenClassification |\n| 394 | multiconer_v2/English (EN) | MultiCoNER/multiconer_v2 | English (EN) | | multiconer | TokenClassification |\n| 395 | multiconer_v2/Farsi (FA) | MultiCoNER/multiconer_v2 | Farsi (FA) | | multiconer | TokenClassification |\n| 396 | multiconer_v2/Portuguese (PT) | MultiCoNER/multiconer_v2 | Portuguese (PT) | | multiconer | TokenClassification |\n| 397 | multiconer_v2/German (DE) | MultiCoNER/multiconer_v2 | German (DE) | | multiconer | TokenClassification |\n| 398 | multiconer_v2/Italian (IT) | MultiCoNER/multiconer_v2 | Italian (IT) | | multiconer | TokenClassification |\n| 399 | multiconer_v2/French (FR) | MultiCoNER/multiconer_v2 | French (FR) | | multiconer | TokenClassification |\n| 400 | mtop | tasksource/mtop | | | mtop | Classification |\n| 401 | multilingual-zero-shot-label-nli | tasksource/multilingual-zero-shot-label-nli | | | mlabel_nli | Classification |\n" }, { "path": "pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools>=45\", \"setuptools_scm[toml]>=6.2\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.setuptools_scm]\n" }, { "path": "setup.cfg", "content": " [metadata]\nname = tasksource\ndescription = Preprocessings to prepare datasets for a task\nlong_description = file: README.md\nlong_description_content_type = text/markdown\nurl = https://github.com/sileod/tasksource/\nclassifiers =\n Programming Language :: Python :: 3\n License :: OSI Approved :: BSD License\n Intended Audience :: Developers\n\n[options]\npackage_dir =\n = src\npackages = find:\npython_requires = >=3.6\ninstall_requires =\n dotwiz\n funcy\n datasets\n exrex\n magicattr\n pandas\n numpy\n scipy\n sorcery\n\n[options.packages.find]\nwhere = src\n" }, { "path": "src/tasksource/.ipynb_checkpoints/access-checkpoint.py", "content": "from .preprocess import Preprocessing\nimport re\nimport pandas as pd\nfrom . import tasks, recast\nfrom .metadata import dataset_rank\nfrom datasets import load_dataset\nimport funcy as fc\nimport os\nimport copy\nfrom sorcery import dict_of\nfrom functools import cache\nimport random\n\n\nclass lazy_mtasks:\n def __getattr__(self, name):\n from . import mtasks\n return getattr(mtasks, name)\n\n def __dir__(self):\n from . import mtasks\n return dir(mtasks)\nlmtasks=lazy_mtasks()\n\ndef parse_var_name(s):\n config_name,task_name = None,None\n if '__' in s and '___' not in s: # dataset__task\n dataset_name, task_name = s.split('__') \n elif '__' not in s.replace('___','') and '___' in s: #dataset___config\n dataset_name, config_name = s.split('___') \n elif '___' in s and '__' in s.split('___')[1]: #dataset___config__task\n dataset_name, config_task=s.split('___')\n config_name,task_name = config_task.split('__')\n else: # dataset \n dataset_name = s\n return dataset_name,config_name,task_name\n\ndef pretty_name(x):\n dn = x.dataset_name.split(\"/\")[-1] \n cn = x.config_name if x.config_name else \"\"\n tn = x.task_name if x.task_name else \"\"\n return f\"{dn}/{cn}/{tn}\".replace('//','/').rstrip('/')\n\n@cache\ndef list_tasks(tasks_path=f'{os.path.dirname(__file__)}/tasks.py',multilingual=False,instruct=False, excluded=[]):\n if multilingual:\n tasks_path=tasks_path.replace('/tasks.py','/mtasks.py')\n task_order = open(tasks_path).readlines()\n task_order = [x.split('=')[0].rstrip() for x in task_order if '=' in x]\n task_order = [x for x in task_order if x.isidentifier()]\n task_order = fc.flip(dict(enumerate(task_order)))\n\n l = []\n _tasks = (lmtasks if multilingual else tasks)\n\n for key in dir(_tasks):\n if key not in task_order:\n continue\n value=getattr(_tasks, key)\n if isinstance(value,Preprocessing):\n dataset_name, config_name, task_name = parse_var_name(key)\n dataset_name = (value.dataset_name if value.dataset_name else dataset_name)\n config_name = (value.config_name if value.config_name else config_name)\n hasattr(value,key)\n l+=[{'dataset_name': dataset_name,\n 'config_name' : config_name,\n 'task_name': task_name,\n 'preprocessing_name': key,\n 'task_type': value.__class__.__name__,'mapping': value,\n 'rank':task_order.get(key,None)}] \n df=pd.DataFrame(l).explode('config_name')\n df = df.sort_values('rank').reset_index(drop=True)\n df['id'] = df.apply(lambda x: pretty_name(x), axis=1)\n df.insert(0, 'id', df.pop('id'))\n del df['rank']\n if instruct:\n df=df[df.id.map(lambda x: not any(a in x for a in recast.improper_labels))]\n df=df[df.id.map(lambda x: not any(x in a for a in excluded))]\n return df\n\n#task_df =list_tasks()\n#mtask_df =list_tasks(multilingual=True)\n\ndef dict_to_query(d=dict(), **kwargs):\n d={**d,**kwargs}\n return '&'.join([f'`{k}`==\"{v}\"' for k,v in d.items()])\n\ndef load_preprocessing(tasks=tasks, **kwargs):\n _tasks_df = list_tasks(multilingual=tasks==lmtasks)\n y = _tasks_df.copy().query(dict_to_query(**kwargs)).iloc[0]\n preprocessing= copy.copy(getattr(tasks, y.preprocessing_name))\n for c in 'dataset_name','config_name':\n if not isinstance(getattr(preprocessing,c), str):\n setattr(preprocessing,c,getattr(y,c))\n return preprocessing\n\ndef load_task(id=None, dataset_name=None,config_name=None,task_name=None,preprocessing_name=None,\n max_rows=None, max_rows_eval=None, multilingual=False, instruct=False, seed=0, **load_dataset_kwargs):\n query = dict_of(id, dataset_name, config_name, task_name,preprocessing_name)\n query = {k:v for k,v in query.items() if v}\n _tasks = (lmtasks if multilingual else tasks)\n preprocessing = load_preprocessing(_tasks, **query)\n\n if \"trust_remote_code\" not in load_dataset_kwargs:\n load_dataset_kwargs[\"trust_remote_code\"] = True\n \n dataset = load_dataset(preprocessing.dataset_name, preprocessing.config_name, **load_dataset_kwargs)\n dataset= preprocessing(dataset,max_rows, max_rows_eval)\n dataset.task_type = preprocessing.__class__.__name__\n if instruct:\n dataset=recast.recast_instruct(dataset)\n return dataset" }, { "path": "src/tasksource/.ipynb_checkpoints/preprocess-checkpoint.py", "content": "from collections.abc import Iterable\nfrom dotwiz import DotWiz\nfrom dataclasses import dataclass\nfrom typing import Union\nimport itertools\nimport funcy as fc\nimport exrex \nimport magicattr \nimport numpy as np\nimport copy\nimport datasets\nimport time\n\nMAX_MC_OPTIONS = 4\n\ndef get_column_names(dataset):\n cn = dataset.column_names\n if type(cn)==dict:\n return set(fc.flatten(cn.values()))\n else:\n return set(cn)\n\n\ndef sample_dataset(dataset,n=10000, n_eval=1000,seed=0):\n for k in dataset:\n n_k=(n if k=='train' else n_eval)\n if n_k and len(dataset[k])>n_k:\n dataset[k]=dataset[k].train_test_split(train_size=n_k,seed=seed)['train']\n return dataset\n\nclass Preprocessing(DotWiz):\n default_splits = ('train','validation','test')\n _instances = []\n\n def __post_init__(self):\n Preprocessing._instances+=[self]\n\n @staticmethod\n def __map_to_target(x,fn=lambda x:None, target=None):\n x[target]=fn(x)\n return x\n \n def load(self):\n return self(datasets.load_dataset(self.dataset_name,self.config_name))\n\n def __call__(self,dataset, max_rows=None, max_rows_eval=None,seed=0):\n dataset = self.pre_process(dataset)\n\n # manage splits\n for k,v in zip(self.default_splits, self.splits):\n if v and k!=v:\n dataset[k]=dataset[v]\n del dataset[v]\n if k in dataset and not v: # obfuscated label\n del dataset[k]\n dataset = fix_splits(dataset)\n\n for k in list(dataset.keys()):\n if k not in self.default_splits:\n del dataset[k]\n dataset = sample_dataset(dataset, max_rows, max_rows_eval,seed=seed)\n \n # field annotated with a string\n substitutions = {v:k for k,v in self.to_dict().items()\n if (k and k not in {'splits','dataset_name','config_name'} \n and type(v)==str and k!=v)}\n\n dataset=dataset.remove_columns([c for c in substitutions.values() if c in dataset['train'].features and c not in substitutions])\n dataset=dataset.rename_columns(substitutions)\n\n # field annotated with a function \n for k in self.to_dict().keys():\n v=getattr(self, k)\n if callable(v) and k not in {\"post_process\",\"pre_process\",\"load\"}:\n dataset=dataset.map(self.__map_to_target,\n fn_kwargs={'fn':v,'target':k})\n\n dataset=dataset.remove_columns(\n get_column_names(dataset)-set(self.to_dict().keys()))\n dataset = fix_labels(dataset)\n dataset = fix_splits(dataset) # again: label mapping changed\n dataset = self.post_process(dataset)\n return dataset\n\n\n@dataclass\nclass cat(Preprocessing):\n fields:Union[str,list]=None\n separator:str=' '\n \n def __call__(self, example=None):\n y=[np.char.array(example[f]) + sep \n for f,sep in zip(self.fields[::-1],itertools.repeat(self.separator))]\n y=list(sum(*y))\n if len(y)==1:\n y=y[0]\n return y\n\n\ndef pretty(f):\n class pretty_f(DotWiz):\n def __init__(self,*args):\n self.__f_arg = f(*args)\n for a in args:\n setattr(self,'value',a)\n \n def __call__(self, *args,**kwargs):\n return self.__f_arg(*args,**kwargs)\n\n def __repr__(self):\n return f\"{self.__f_arg.__qualname__ .split('.')[0]}({self.value})\"\n return pretty_f\n\nclass dotgetter:\n def __init__(self, path=''):\n self.path=path\n\n def __bool__(self):\n return bool(self.path)\n\n def __getattr__(self, k):\n return self.__class__(f'{self.path}.{k}'.lstrip('.'))\n \n def __getitem__(self, i):\n return self.__class__(f'{self.path}[{i}]')\n\n def __call__(self, example=None):\n return magicattr.get(DotWiz(example), self.path)\n\n def __hash__(self):\n return hash(self.path)\n\n\n@dataclass\nclass ClassificationFields(Preprocessing):\n sentence1:str='sentence1'\n sentence2:str='sentence2'\n labels:str='labels'\n\n@dataclass\nclass Seq2SeqLMFields(Preprocessing):\n prompt:str='prompt'\n output:str='output'\n\n@dataclass\nclass TokenClassificationFields(Preprocessing):\n tokens:str='tokens'\n labels:str='labels'\n \n@dataclass\nclass MultipleChoiceFields(Preprocessing):\n inputs:str='input'\n choices:Iterable=tuple()\n labels:str='labels'\n choices_list:str=None\n def __post_init__(self):\n for i, c in enumerate(self.choices):\n setattr(self,f'choice{i}',c)\n delattr(self,'choices')\n if not self.choices_list:\n delattr(self,'choices_list')\n \n def __call__(self,dataset, *args, **kwargs):\n dataset = super().__call__(dataset, *args, **kwargs)\n if self.choices_list:\n dataset = dataset.filter(lambda x: 10),\n dataset_name=\"openai/webgpt_comparisons\")\n\nsynthetic_instruct = MultipleChoice('prompt', choices=['chosen', 'rejected'],\n labels=constant(0), dataset_name=\"Dahoas/synthetic-instruct-gptj-pairwise\")\n\nscruples = Classification(\"text\",labels=\"binarized_label\",dataset_name=\"metaeval/scruples\")\n\nwouldyourather = MultipleChoice(constant('Most people would rather:'), choices=['option_a','option_b'],\n labels= lambda x: int(x['votes_a']0.1].UID)\ndel dfh, dfm, df\n\nblimp_groups = {\n \"syntax\": [\n \"adjunct_island\",\n \"animate_subject_passive\",\n \"animate_subject_trans\",\n \"causative\",\n \"complex_NP_island\",\n \"coordinate_structure_constraint_complex_left_branch\",\n \"coordinate_structure_constraint_object_extraction\",\n \"drop_argument\",\n \"ellipsis_n_bar_1\",\n \"ellipsis_n_bar_2\",\n \"inchoative\",\n \"intransitive\",\n \"left_branch_island_echo_question\",\n \"left_branch_island_simple_question\",\n \"passive_1\",\n \"passive_2\",\n \"sentential_subject_island\",\n \"transitive\",\n \"wh_island\",\n \"wh_questions_object_gap\",\n \"wh_questions_subject_gap\",\n \"wh_questions_subject_gap_long_distance\",\n \"wh_vs_that_no_gap\",\n \"wh_vs_that_no_gap_long_distance\",\n \"wh_vs_that_with_gap\",\n \"wh_vs_that_with_gap_long_distance\"\n ],\n \"morphology\": [\n \"anaphor_gender_agreement\",\n \"anaphor_number_agreement\",\n \"determiner_noun_agreement_1\",\n \"determiner_noun_agreement_2\",\n \"determiner_noun_agreement_irregular_1\",\n \"determiner_noun_agreement_irregular_2\",\n \"determiner_noun_agreement_with_adj_2\",\n \"determiner_noun_agreement_with_adj_irregular_1\",\n \"determiner_noun_agreement_with_adj_irregular_2\",\n \"determiner_noun_agreement_with_adjective_1\",\n \"distractor_agreement_relational_noun\",\n \"distractor_agreement_relative_clause\",\n \"irregular_past_participle_adjectives\",\n \"irregular_past_participle_verbs\",\n \"irregular_plural_subject_verb_agreement_1\",\n \"irregular_plural_subject_verb_agreement_2\",\n \"regular_plural_subject_verb_agreement_1\",\n \"regular_plural_subject_verb_agreement_2\"\n ],\n \"syntax_semantics\": [\n \"existential_there_object_raising\",\n \"existential_there_subject_raising\",\n \"expletive_it_object_raising\",\n \"only_npi_scope\",\n \"principle_A_c_command\",\n \"principle_A_case_1\",\n \"principle_A_domain_1\",\n \"principle_A_domain_2\",\n \"principle_A_domain_3\",\n \"principle_A_reconstruction\",\n \"sentential_negation_npi_scope\",\n \"tough_vs_raising_1\",\n \"tough_vs_raising_2\"\n ],\n \"semantics\": [\n \"existential_there_quantifiers_1\",\n \"existential_there_quantifiers_2\",\n \"matrix_question_npi_licensor_present\",\n \"npi_present_1\",\n \"npi_present_2\",\n \"only_npi_licensor_present\",\n \"sentential_negation_npi_licensor_present\",\n \"superlative_quantifiers_1\",\n \"superlative_quantifiers_2\"\n ],\n \"syntax/semantics\": [\n \"principle_A_case_2\"\n ]\n}\n" }, { "path": "src/tasksource/metadata/original.txt", "content": "WANLI\nrecast/recast_verbnet\nrecast/recast_verbcorner\nrecast/recast_ner\nrecast/recast_sentiment\nrecast/recast_puns\nrecast/recast_factuality\nrecast/recast_megaveridicality\nprobability_words_nli/reasoning_1hop\nprobability_words_nli/usnli\nprobability_words_nli/reasoning_2hop\nnan-nli/joey234--nan-nli\nnli_fever\nbreaking_nli\nconj_nli\nfracas\ndialogue_nli\nmpe\ndnc\nrecast_white/fnplus\nrecast_white/sprl\nrecast_white/dpr\nrobust_nli/IS_CS\nrobust_nli/LI_LI\nrobust_nli/ST_WO\nrobust_nli/PI_SP\nrobust_nli/PI_CD\nrobust_nli/ST_SE\nrobust_nli/ST_NE\nrobust_nli/ST_LM\nrobust_nli_is_sd\nrobust_nli_li_ts\ngen_debiased_nli/snli_seq_z\ngen_debiased_nli/snli_z_aug\ngen_debiased_nli/snli_par_z\ngen_debiased_nli/mnli_par_z\ngen_debiased_nli/mnli_z_aug\ngen_debiased_nli/mnli_seq_z\nadd_one_rte\nhlgd\nconll2003/pos_tags\nconll2003/chunk_tags\nconll2003/ner_tags\nhh-rlhf\nmodel-written-evals\nfig-qa\nsocial_i_qa\nbalanced-copa\ne-CARE\ninsincere-questions\nTuringBench\nvitaminc/tals--vitaminc\nrumoureval_2019/RumourEval2019\ntweet_eval/irony\ntweet_eval/stance_abortion\ntweet_eval/hate\ntweet_eval/stance_atheism\ntweet_eval/stance_climate\ntweet_eval/emoji\ntweet_eval/offensive\ntweet_eval/sentiment\ntweet_eval/emotion\ntweet_eval/stance_feminist\ntweet_eval/stance_hillary\ndiscovery/discovery\npragmeval/verifiability\npragmeval/mrda\npragmeval/switchboard\npragmeval/emergent\npragmeval/gum\npragmeval/sarcasm\npragmeval/stac\npragmeval/pdtb\nsilicone/dyda_e\nsilicone/oasis\nsilicone/meld_s\nsilicone/meld_e\nsilicone/maptask\nsilicone/dyda_da\nsilicone/sem\nsilicone/iemocap\nlex_glue/scotus\nlex_glue/ledgar\nlanguage-identification\nrotten_tomatoes\nhate_speech18\nsms_spam\nsnips_built_in_intents\nhate_speech_offensive\nhyperpartisan_news\nsciie\ncitation_intent\nscicite\nlexical_relation_classification/ROOT09\nlexical_relation_classification/CogALexV\nlexical_relation_classification/K&H+N\nlexical_relation_classification/BLESS\nlexical_relation_classification/EVALution\ncrowdflower/political-media-bias\ncrowdflower/tweet_global_warming\ncrowdflower/text_emotion\ncrowdflower/political-media-message\ncrowdflower/political-media-audience\ncrowdflower/economic-news\ncrowdflower/corporate-messaging\ncrowdflower/airline-sentiment\ncrowdflower/sentiment_nuclear_power\nethics/commonsense\nethics/deontology\nethics/justice\nethics/virtue\ntweets_hate_speech_detection\nwnut_17/wnut_17\nncbi_disease/ncbi_disease\nacronym_identification\njnlpba/jnlpba\nontonotes_english/SpeedOfMagic--ontonotes_english\nblog_authorship_corpus/gender\nblog_authorship_corpus/horoscope\nblog_authorship_corpus/job\nopen_question_type\nmc_taco\ndiscosense\nEffectiveFeedbackStudentWriting\nphrase_similarity\nscientific-exaggeration-detection\nfever-evidence-related/mwong--fever-related\ndynasent/dynabench.dynasent.r1.all/r1\ndynasent/dynabench.dynasent.r2.all/r2\nsem_eval_2010_task_8\nmedmcqa\nlogiqa\ncycic_classification\ncycic_multiplechoice\ncommonsense_qa_2.0\nlingnli\nmonotonicity-entailment\narct\nscinli\nnaturallogic\nonestop_qa\nmoral_stories/full\nprost\ndynahate\nsyntactic-augmentation-nli\nautotnli\nCONDAQA\nwebgpt_comparisons\nsynthetic-instruct-gptj-pairwise\nscruples\nwouldyourather\nattempto-nli\ndefeasible-nli/snli\ndefeasible-nli/atomic\nhelp-nli\nnli-veridicality-transitivity\nnatural-language-satisfiability\nlonli\ndadc-limit-nli\nFLUTE\nsummarize_from_feedback/comparisons\nfolio\ntomi-nli\navicenna\nSHP\nMedQA-USMLE-4-options-hf\nwikimedqa/medwiki\ncicero\nmutual\nNeQA\nquote-repetition\nredefine-math\npuzzte\nimplicatures\nrace-c\nspartqa-yn\nspartqa-mchoice\ntemporal-nli\nriddle_sense\nclcd-english\ntwentyquestions\nreclor\ncounterfactually-augmented-imdb\ncounterfactually-augmented-snli\ncnli\nboolq-natural-perturbations\nequate\nScienceQA_text_only\nekar_english\nimplicit-hate-stg1\nlogiqa-2.0-nli\nPARARULE-Plus\nmindgames\nuniversal_dependencies/en_partut/deprel\nuniversal_dependencies/en_lines/deprel\nuniversal_dependencies/en_gum/deprel\nuniversal_dependencies/en_ewt/deprel\nambient\npath-naturalness-prediction\ncloth\ndgen\noasst1_pairwise_rlhf_reward\nI2D2\nargs_me\nTouche23-ValueEval\nstarcon\nbanking77\nruletaker\nlsat_qa/all\nConTRoL-nli\ntracie\nsherliic\nsen-making/1\nsen-making/2\nmbib-base/cognitive-bias\nmbib-base/fake-news\nmbib-base/gender-bias\nmbib-base/hate-speech\nmbib-base/linguistic-bias\nmbib-base/political-bias\nmbib-base/racial-bias\nmbib-base/text-level-bias\nrobustLR\nv1/gen_train234_test2to10\nlogical-fallacy\nparade\ncladder\nsubjectivity\nMOH\nVUAC\nTroFi\nsharc_modified/mod\nconceptrules_v2\ndisrpt/eng.dep.scidtb\nconll2000\nfew-nerd/supervised\ncom2sense\nscone\nwinodict\nfool-me-twice\nmonli\ncorr2cause\napt\ntwitter-financial-news-sentiment\nSpaceNLI\npropsegment/nli\nHatemojiBuild\nregset\nesci\ndnd_style_intents\n" }, { "path": "src/tasksource/metadata/popularity.py", "content": "dataset_rank = {'glue': 0,\r\n 'super_glue': 12,\r\n 'tweet_eval': 23,\r\n 'blimp': 34,\r\n 'imdb': 101,\r\n 'wikitext': 102,\r\n 'squad': 106,\r\n 'trec': 107,\r\n 'openwebtext': 108,\r\n 'rotten_tomatoes': 109,\r\n 'anli': 110,\r\n 'adversarial_qa': 111,\r\n 'ai2_arc': 115,\r\n 'xsum': 117,\r\n 'amazon_reviews_multi': 118,\r\n 'ag_news': 125,\r\n 'yelp_review_full': 126,\r\n 'wino_bias': 127,\r\n 'piqa': 131,\r\n 'duorc': 132,\r\n 'quail': 134,\r\n 'trivia_qa': 135,\r\n 'cnn_dailymail': 143,\r\n 'common_gen': 146,\r\n 'sst': 147,\r\n 'conll2003': 150,\r\n 'financial_phrasebank': 151,\r\n 'babi_qa': 155,\r\n 'poem_sentiment': 163,\r\n 'dream': 164,\r\n 'paws': 165,\r\n 'emotion': 168,\r\n 'kilt_tasks': 169,\r\n 'sciq': 180,\r\n 'cos_e': 181,\r\n 'dbpedia_14': 183,\r\n 'newsgroup': 184,\r\n 'cosmos_qa': 244,\r\n 'squad_v2': 245,\r\n 'samsum': 246,\r\n 'amazon_polarity': 247,\r\n 'multi_news': 248,\r\n 'wiki_hop': 249,\r\n 'quartz': 251,\r\n 'qasc': 252,\r\n 'wiki_qa': 253,\r\n 'openbookqa': 254,\r\n 'ropes': 256,\r\n 'quoref': 257,\r\n 'snli': 258,\r\n 'app_reviews': 259,\r\n 'gigaword': 260,\r\n 'wiki_bio': 261,\r\n 'amazon_us_reviews': 262,\r\n 'scan': 308,\r\n 'race': 320,\r\n 'swag': 323,\r\n 'codah': 325,\r\n 'ccdv/arxiv-summarization': 331,\r\n 'subjqa': 333,\r\n 'universal_morphologies': 339,\r\n 'hans': 447,\r\n 'sst2': 448,\r\n 'guardian_authorship': 449,\r\n 'math_qa': 465,\r\n 'librispeech_asr': 466,\r\n 'hendrycks_test': 469,\r\n 'openai_humaneval': 526,\r\n 'ptb_text_only': 527,\r\n 'pubmed_qa': 528,\r\n 'head_qa': 531,\r\n 'ought/raft': 533,\r\n 'ade_corpus_v2': 544,\r\n 'cbt': 547,\r\n 'bookcorpus': 552,\r\n 'squadshifts': 553,\r\n 'story_cloze': 557,\r\n 'multi_nli': 559,\r\n 'qanta': 560,\r\n 'hate_speech18': 564,\r\n 'gem': 565,\r\n 'lex_glue': 599,\r\n 'deepmind/code_contests': 606,\r\n 'imagenet-1k': 607,\r\n 'blended_skill_talk': 608,\r\n 'sms_spam': 609,\r\n 'asset': 610,\r\n 'fever': 612,\r\n 'commonsense_qa': 615,\r\n 'scientific_papers': 616,\r\n 'evidence_infer_treatment': 618,\r\n 'hotpot_qa': 620,\r\n 'superb': 622,\r\n 'sick': 628,\r\n 'humicroedit': 629,\r\n 'snips_built_in_intents': 631,\r\n 'winograd_wsc': 632,\r\n 'bigbench': 634,\r\n 'multi_woz_v22': 801,\r\n 'lambada': 803,\r\n 'banking77': 804,\r\n 'hate_speech_offensive': 805,\r\n 'yahoo_answers_topics': 806,\r\n 'ccdv/cnn_dailymail': 807,\r\n 'hyperpartisan_news_detection': 810,\r\n 'gsm8k': 812,\r\n 'wikisql': 814,\r\n 'the_pile': 815,\r\n 'health_fact': 825,\r\n 'mdd': 826,\r\n 'web_questions': 830,\r\n 'ethos': 831,\r\n 'wnut_17': 833,\r\n 'medical_questions_pairs': 834,\r\n 'scitldr': 835,\r\n 'drop': 838,\r\n 'squad_adversarial': 839,\r\n 'e2e_nlg_cleaned': 841,\r\n 'onestop_english': 842,\r\n 'pragmeval': 843,\r\n 'relbert/analogy_questions': 863,\r\n 'nq_open': 869,\r\n 'daily_dialog': 870,\r\n 'mc_taco': 871,\r\n 'crows_pairs': 872,\r\n 'go_emotions': 873,\r\n 'ncbi_disease': 875,\r\n 'boolq': 876,\r\n 'movie_rationales': 877,\r\n 'climate_fever': 878,\r\n 'discovery': 879,\r\n 'lama': 881,\r\n 'ecthr_cases': 885,\r\n 'jfleg': 887,\r\n 'selqa': 888,\r\n 'acronym_identification': 892,\r\n 'scicite': 893,\r\n 'tab_fact': 894,\r\n 'wiki_asp': 896,\r\n 'enriched_web_nlg': 916,\r\n 'svhn': 918,\r\n 'docred': 920,\r\n 'conllpp': 921,\r\n 'liar': 922,\r\n 'multi_x_science_sum': 923,\r\n 'discofuse': 924,\r\n 'competition_math': 926,\r\n 'biosses': 927,\r\n 'jnlpba': 928,\r\n 'web_nlg': 929,\r\n 'qa_srl': 937,\r\n 'neural_code_search': 938,\r\n 'conv_ai_2': 940,\r\n 'craigslist_bargains': 941,\r\n 'qed': 942,\r\n 'conv_ai_3': 943,\r\n 'conv_ai': 944,\r\n 'turk': 945,\r\n 'covid_qa_castorini': 946,\r\n 'sem_eval_2014_task_1': 947,\r\n 'mwsc': 948,\r\n 'gutenberg_time': 949,\r\n 'billsum': 950,\r\n 'riddle_sense': 951,\r\n 'species_800': 952,\r\n 'hlgd': 953,\r\n 'definite_pronoun_resolution': 954,\r\n 'tmu_gfm_dataset': 955,\r\n 'relbert/semeval2012_relational_similarity_v4': 956,\r\n 'clinc_oos': 957,\r\n 'imppres': 960,\r\n 'mrqa': 976,\r\n 'cc_news': 977,\r\n 'lmqg/qag_tweetqa': 978,\r\n 'aeslc': 979,\r\n 'big_patent': 980,\r\n 'eli5': 990,\r\n 'scene_parse_150': 991,\r\n 'circa': 993,\r\n 'aqua_rat': 994,\r\n 'nlu_evaluation_data': 996,\r\n 'newspop': 997,\r\n 'relbert/lexical_relation_classification': 998,\r\n 'yahoo_answers_qa': 1003,\r\n 'emo': 1004,\r\n 'silicone': 1005,\r\n 'cord19': 1015,\r\n 'tweet_qa': 1018,\r\n 'meta_woz': 1019,\r\n 'md_gender_bias': 1021,\r\n 'art': 1031,\r\n 'google_wellformed_query': 1032,\r\n 'ambig_qa': 1033,\r\n 'taskmaster2': 1035,\r\n 'quac': 1042,\r\n 'freebase_qa': 1043,\r\n 'quora': 1044,\r\n 'numer_sense': 1045,\r\n 'narrativeqa': 1046,\r\n 'ccdv/pubmed-summarization': 1047,\r\n 'qa_zre': 1049,\r\n 'limit': 1050,\r\n 'tweets_hate_speech_detection': 1051,\r\n 'mocha': 1052,\r\n 'hatexplain': 1053,\r\n 'bing_coronavirus_query_set': 1054,\r\n 'great_code': 1055,\r\n 'medal': 1056,\r\n 'sent_comp': 1057,\r\n 'kelm': 1058,\r\n 'natural_questions': 1059,\r\n 'wiki_split': 1061,\r\n 'zest': 1062,\r\n 'cfq': 1063,\r\n 'multi_re_qa': 1071,\r\n 'stereoset': 1080,\r\n 'coqa': 1082,\r\n 'cuad': 1083,\r\n 'break_data': 1084,\r\n 'mbpp': 1089,\r\n 'knkarthick/dialogsum': 1091,\r\n 'wiki_auto': 1092,\r\n 'pile-of-law/pile-of-law': 1097,\r\n 'pg19': 1132,\r\n 'DFKI-SLT/few-nerd': 1133,\r\n 'wikicorpus': 1136,\r\n 'e2e_nlg': 1142,\r\n 'anton-l/superb': 1143,\r\n 'ghomasHudson/muld': 1144,\r\n 'Exr0n/wiki-entity-similarity': 1150,\r\n 'BeIR/nfcorpus': 1156,\r\n 'ccdv/govreport-summarization': 1158,\r\n 'woz_dialogue': 1159,\r\n 'reddit': 1164,\r\n 'EMBO/sd-nlp': 1165,\r\n 'empathetic_dialogues': 1170,\r\n 'BeIR/fiqa': 1171,\r\n 'generics_kb': 1173,\r\n 'swda': 1177,\r\n 'wikitablequestions': 1178,\r\n 'pubmed': 1183,\r\n 'chr_en': 1184,\r\n 'sharc': 1185,\r\n 'sharc_modified': 1186,\r\n 'BeIR/scifact': 1190,\r\n 'nell': 1192,\r\n 'patriziobellan/PET': 1196,\r\n 'EMBO/biolang': 1198,\r\n 'dynabench/qa': 1202,\r\n 'reddit_tifu': 1206,\r\n 'BeIR/scidocs': 1208,\r\n 'pec': 1210,\r\n 'tner/tweetner7': 1213,\r\n 'BeIR/arguana': 1214,\r\n 'multidoc2dial': 1216,\r\n 'taskmaster1': 1219,\r\n 'spider': 1221,\r\n 'adv_glue': 1222,\r\n 'allenai/mslr2022': 1228,\r\n 'conceptnet5': 1230,\r\n 'tyqiangz/multilingual-sentiments': 1233,\r\n 'newsqa': 1246,\r\n 'metashift': 1249,\r\n 'so_stacksample': 1250,\r\n 'doc2dial': 1253,\r\n 'search_qa': 1256,\r\n 'yhavinga/mc4_nl_cleaned': 1258,\r\n 'hope_edi': 1270,\r\n 'proto_qa': 1273,\r\n 'tuple_ie': 1276,\r\n 'simple_questions_v2': 1279,\r\n 'nlpaueb/finer-139': 1282,\r\n 'bookcorpusopen': 1283,\r\n 'tner/ontonotes5': 1284,\r\n 'crd3': 1285,\r\n 'ucberkeley-dlab/measuring-hate-speech': 1286,\r\n 'gap': 1287,\r\n 'recipe_nlg': 1288,\r\n 'schema_guided_dstc8': 1289,\r\n 'BeIR/beir': 1291,\r\n 'sagnikrayc/mctest': 1294,\r\n 'eurlex': 1296,\r\n 'corypaik/coda': 1297,\r\n 'bc2gm_corpus': 1298,\r\n 'ascent_kb': 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'strombergnlp/rumoureval_2019': 2007,\r\n 'rongzhangibm/NaturalQuestionsV2': 2008,\r\n 'Muennighoff/mbpp': 2009,\r\n 'RUCAIBox/Simplification': 2011,\r\n 'shubhamg2208/lexicap': 2012,\r\n 'olm/olm-wikipedia-20220701': 2013,\r\n 'esc-bench/esc-diagnostic-dataset': 2014,\r\n 'jpwahle/autoregressive-paraphrase-dataset': 2015,\r\n 'GabrielVidal/dead-by-daylight-perks': 2016,\r\n 'DTU54DL/common-proc-whisper': 2017,\r\n 'valurank/PoliticalBias': 2018,\r\n 'McGill-NLP/TopiOCQA': 2019,\r\n 'gsarti/magpie': 2020,\r\n 'BeIR/cqadupstack-generated-queries': 2021,\r\n 'MicPie/unpredictable_mmo-champion-com': 2022,\r\n 'RUCAIBox/Question-Generation': 2023,\r\n 'allenai/multinews_sparse_mean': 2024,\r\n 'demo-org/diabetes': 2025,\r\n 'StonyBrookNLP/tellmewhy': 2026,\r\n 'bergr7/weakly_supervised_ag_news': 2027,\r\n 'din0s/msmarco-nlgen': 2028,\r\n 'frankier/cross_domain_reviews': 2029,\r\n 'gart-labor/pumpnli': 2030,\r\n 'AndyChiang/cloth': 2031,\r\n 'olm/olm-CC-MAIN-2017-22-sampling-ratio-0.16178770949': 2032,\r\n 'bgstud/libri': 2033,\r\n 'DTU54DL/commonvoice_accent_test': 2034,\r\n 'lewtun/my-awesome-dataset': 2035,\r\n 'peixian/rtGender': 2036,\r\n 'pmc/open_access': 2039,\r\n 'uva-irlab/trec-cast-2019-multi-turn': 2043,\r\n 'DFKI-SLT/scidtb': 2044,\r\n 'surrey-nlp/PLOD-filtered': 2045,\r\n 'wanyu/IteraTeR_v2': 2046,\r\n 'strombergnlp/ipm_nel': 2047,\r\n 'HuggingFaceM4/charades': 2048,\r\n 'ncats/EpiSet4NER-v2': 2050,\r\n 'HuggingFaceM4/ActivitiyNet_Captions': 2051,\r\n 'sileod/discourse_marker_qa': 2052,\r\n 'yoshitomo-matsubara/srsd-feynman_medium': 2053,\r\n 'BeIR/nfcorpus-generated-queries': 2054,\r\n 'BeIR/trec-news-generated-queries': 2055,\r\n 'BeIR/robust04-generated-queries': 2056,\r\n 'BeIR/quora-generated-queries': 2057,\r\n 'valurank/Adult-content-dataset': 2058,\r\n 'launch/open_question_type': 2059,\r\n 'knkarthick/topicsum': 2060,\r\n 'yuningm/citesum': 2061,\r\n 'elihoole/asrs-aviation-reports': 2062,\r\n 'DeveloperOats/DBPedia_Classes': 2063,\r\n 'hoskinson-center/proof-pile': 2064,\r\n 'RUCAIBox/Summarization': 2065,\r\n 'RUCAIBox/Question-Answering': 2066,\r\n 'RUCAIBox/Story-Generation': 2067,\r\n 'RUCAIBox/Paraphrase': 2068,\r\n 'jakartaresearch/semeval-absa': 2069,\r\n 'tner/ttc_dummy': 2071,\r\n 'copenlu/citeworth': 2072,\r\n 'allenai/multinews_sparse_oracle': 2073,\r\n 'allenai/multixscience_sparse_oracle': 2074,\r\n 'allenai/multixscience_sparse_mean': 2075,\r\n 'allenai/multixscience_sparse_max': 2076,\r\n 'allenai/ms2_sparse_oracle': 2077,\r\n 'mschi/blogspot_raw': 2078,\r\n 'gaurikapse/civis-consultation-summaries': 2079,\r\n 'chenghao/cuad_qa': 2080,\r\n 'esc-bench/esc-datasets': 2081,\r\n 'olm/olm-wikipedia-20221001': 2082,\r\n 'allenai/wcep_dense_oracle': 2083,\r\n 'dennlinger/wiki-paragraphs': 2084,\r\n 'AndyChiang/dgen': 2085,\r\n 'esb/diagnostic-dataset': 2086,\r\n 'havens2/naacl2022': 2087,\r\n 'fkdosilovic/docee-event-classification': 2088,\r\n 'DTU54DL/demo-common-whisper': 2089,\r\n 'dferndz/cSQuAD1': 2090,\r\n 'jpcorb20/multidogo': 2091,\r\n 'julien-c/reactiongif': 2092,\r\n 'lara-martin/Scifi_TV_Shows': 2093,\r\n 'lukesjordan/worldbank-project-documents': 2094,\r\n 'mnemlaghi/widdd': 2095,\r\n 'mvarma/medwiki': 2096,\r\n 'nateraw/beans': 2098,\r\n 'nateraw/cats_vs_dogs': 2099,\r\n 'nateraw/food101': 2100,\r\n 'nateraw/sync_food101': 2101,\r\n 'ncats/EpiSet4BinaryClassification': 2102,\r\n 'ncats/EpiSet4NER-v1': 2103,\r\n 'peixian/equity_evaluation_corpus': 2104,\r\n 'rajeshradhakrishnan/malayalam_wiki': 2105,\r\n 'softcatala/open-source-english-catalan-corpus': 2106,\r\n 'toloka/CrowdSpeech': 2107,\r\n 'valurank/12-factor': 2108,\r\n 'valurank/PoliticalBias_AllSides_Txt': 2109,\r\n 'valurank/PoliticalBias_Sources': 2110,\r\n 'valurank/hate-multi': 2111,\r\n 'valurank/news-12factor': 2112,\r\n 'valurank/offensive-multi': 2113,\r\n 'webimmunization/COVID-19-vaccine-attitude-tweets': 2114,\r\n 'wpicard/nostradamus-propheties': 2115,\r\n 'yuanchuan/annotated_reference_strings': 2116,\r\n 'ruanchaves/stan_large': 2117,\r\n 'ruanchaves/stan_small': 2118,\r\n 'ruanchaves/boun': 2119,\r\n 'ruanchaves/dev_stanford': 2120,\r\n 'ruanchaves/test_stanford': 2121,\r\n 'ruanchaves/snap': 2122,\r\n 'z-uo/qasper-squad': 2123,\r\n 'SocialGrep/the-antiwork-subreddit-dataset': 2124,\r\n 'CLUTRR/v1': 2126,\r\n 'malteos/test2': 2132,\r\n 'TomTBT/pmc_open_access_xml': 2133,\r\n 'SocialGrep/the-reddit-dataset-dataset': 2137,\r\n 'SocialGrep/the-reddit-place-dataset': 2139,\r\n 'projecte-aina/gencata': 2141,\r\n 'mwong/climate-evidence-related': 2142,\r\n 'mwong/climate-claim-related': 2143,\r\n 'surrey-nlp/PLOD-unfiltered': 2144,\r\n 'SocialGrep/the-reddit-irl-dataset': 2145,\r\n 'Lexi/spanextract': 2147,\r\n 'mwong/climatetext-claim-related-evaluation': 2148,\r\n 'mwong/climatetext-evidence-related-evaluation': 2149,\r\n 'ylacombe/xsum_factuality': 2150,\r\n 'mwong/climatetext-climate_evidence-claim-related-evaluation': 2151,\r\n 'mwong/climatetext-claim-climate_evidence-related-evaluation': 2152,\r\n 'mwong/climatetext-evidence-claim-pair-related-evaluation': 2153,\r\n 'mwong/climatetext-claim-evidence-pair-related-evaluation': 2154,\r\n 'patrickvonplaten/librispeech_asr_self_contained': 2155,\r\n 'BritishLibraryLabs/web_archive_classification': 2158,\r\n 'albertxu/CrosswordQA': 2159,\r\n 'SocialGrep/the-reddit-nft-dataset': 2160,\r\n 'janck/bigscience-lama': 2162,\r\n 'strombergnlp/twitter_pos_vcb': 2163,\r\n 'Filippo/osdg_cd': 2164,\r\n 'Ukhushn/home-depot': 2165,\r\n 'pile-of-law/eoir_privacy': 2166,\r\n 'drAbreu/sd-nlp-2': 2168,\r\n 'Leyo/TGIF': 2173,\r\n 'strombergnlp/named_timexes': 2174,\r\n 'domenicrosati/TruthfulQA': 2175,\r\n 'Roh/ryanspeech': 2176,\r\n 'Leyo/ActivityNet_Captions': 2177,\r\n 'IsaacBot/SQuAD-single-sentence-QA': 2178,\r\n 'morteza/cogtext': 2179,\r\n 'wdc/products-2017': 2180,\r\n 'rajeshvarma/QA_on_SLA': 2196,\r\n 'statworx/haiku': 2197,\r\n 'rajistics/million-headlines': 2198,\r\n 'feyzaakyurek/BBNLI': 2199,\r\n 'launch/gov_report_qs': 2200,\r\n 'DFKI-SLT/wikitext_linked': 2202,\r\n 'dianalogan/Marketing-Budget-and-Actual-Sales-Dataset': 2204,\r\n 'mehnaazasad/arxiv-co-ga': 2205,\r\n 'JeremyAlain/123_test': 2206,\r\n 'BeIR/arguana-generated-queries': 2209,\r\n 'BeIR/climate-fever-generated-queries': 2210,\r\n 'BeIR/dbpedia-entity-generated-queries': 2211,\r\n 'wise-east/spolin': 2212,\r\n 'yoshitomo-matsubara/srsd-feynman_hard': 2213,\r\n 'florentgbelidji/edmunds-car-ratings': 2214,\r\n 'olivierdehaene/xkcd': 2215,\r\n 'rajistics/auditor_review': 2216,\r\n 'BeIR/scifact-generated-queries': 2217,\r\n 'BeIR/trec-covid-generated-queries': 2218,\r\n 'BeIR/webis-touche2020-generated-queries': 2219,\r\n 'BeIR/nq-generated-queries': 2220,\r\n 'BeIR/hotpotqa-generated-queries': 2221,\r\n 'BeIR/bioasq-generated-queries': 2222,\r\n 'icelab/ntrs_meta': 2223,\r\n 'iejMac/CLIP-Kinetics700': 2224,\r\n 'fever/feverous': 2225,\r\n 'Livingwithmachines/hmd-erwt-training': 2226,\r\n 'wkrl/cord': 2227,\r\n 'launch/reddit_qg': 2228,\r\n 'arize-ai/xtreme_en': 2229}\r\n\r\ndataset_rank['Anthropic/model-written-evals']=13\r\ndataset_rank['Anthropic/hh-rlhf']=14" }, { "path": "src/tasksource/mtasks.py", "content": "from .preprocess import cat, get,name, regen, constant, Classification, TokenClassification, MultipleChoice\nfrom .metadata import udep_labels\nfrom datasets import get_dataset_config_names, ClassLabel, Dataset, DatasetDict, concatenate_datasets, Sequence\n\ndef all(dataset_name):\n try:\n config_name=get_dataset_config_names(dataset_name)\n except Exception as e:\n print(dataset_name,e)\n config_name=None\n return dict(dataset_name=dataset_name, config_name=config_name)\n\ndef concatenate_configs(dataset):\n return DatasetDict(train=concatenate_datasets(list(dataset.values())))\n\n# english tasks (few, to keep balance between languages)\n\nmoritz_xnli = Classification(\"premise\",\"hypothesis\",name(\"label\",[\"entailment\", \"neutral\",\"contradiction\"]), \n pre_process=concatenate_configs, \n dataset_name=\"MoritzLaurer/multilingual-NLI-26lang-2mil7\")\n\nxnli = Classification(\"premise\", \"hypothesis\", \"label\", **all(\"metaeval/xnli\"))\n\namericas_nli = Classification(\"premise\",\"hypothesis\",\"label\",config_name=\"all_languages\")\n\nstsb_multi_mt = Classification(\"sentence1\", \"sentence2\",\n lambda x: float(x[\"similarity_score\"]/5),\n **all('stsb_multi_mt'))\n\npawsx = Classification(\"sentence1\",\"sentence2\",name('label',['not_paraphrase','paraphrase']), **all('paws-x'))\n\nmiam = Classification(\"Utterance\",labels=\"Label\", **all('miam'))\n\nxstance = Classification(\"question\", \"comment\", \"label\",\n **all(\"strombergnlp/x-stance\"))\n\n\noffenseval = Classification(lambda x: str(x[\"text\"]), labels=name(\"subtask_a\",['not offensive','offensive']),\n pre_process=lambda ds:ds.filter(lambda x: x['subtask_a'] in [0,1]),\n dataset_name='strombergnlp/offenseval_2020',\n config_name=[\"ar\",\"da\",\"gr\",\"tr\"])\n\noffenseval_dravidian = Classification(\"text\",labels=\"label\",config_name=['kannada','malayalam','tamil'])\n\nmlma_hate = Classification(\"tweet\", labels=lambda x:x[\"sentiment\"].split('_'),\n dataset_name=\"nedjmaou/MLMA_hate_speech\")\n\nqam = Classification(\"question\",\"answer\",\"label\", dataset_name=\"xglue\",config_name=\"qam\")\n\n#x_sum_factuality = Classification(\"summary\",\"generated_summary\",\"label\", dataset_name=\"ylacombe/xsum_factuality\")\n\nx_fact = Classification('evidence','claim','label', dataset_name=\"metaeval/x-fact\")\n\nxglue___nc = Classification('news_body',labels='news_category')\nxglue___qadsm = Classification('query','ad_description','relevance_label')\nxglue___qam = Classification('question','answer','label')\nxglue___wpr = Classification('query','web_page_snippet','relavance_label') # relavance_label : sic\n\nxlwic = Classification(\n sentence1=cat([\"target_word\",\"context_1\"], \" : \"),\n sentence2=cat([\"target_word\",\"context_2\"], \" : \"),\n labels='label',dataset_name=\"pasinit/xlwic\",config_name=['xlwic_de_de','xlwic_it_it','xlwic_fr_fr','xlwic_en_ko'])\n\n#[ \"spam\", \"fails_task\", \"lang_mismatch\", \"pii\", \"not_appropriate\", \"hate_speech\", \"sexual_content\", \"quality\", \"toxicity\", \"humor\", \"helpfulness\", \"creativity\", \"violence\" ]\n\noasst1__quality = Classification(\"parent_text\",\"text\",labels=\"quality\", dataset_name=\"tasksource/oasst1_dense_flat\",\n pre_process = lambda ds:ds.remove_columns('labels'))\noasst1__toxicity = Classification(\"parent_text\",\"text\",labels=\"toxicity\", dataset_name=\"tasksource/oasst1_dense_flat\",\n pre_process = lambda ds:ds.remove_columns('labels'))\noasst1__helpfulness = Classification(\"parent_text\",\"text\",labels=\"helpfulness\", dataset_name=\"tasksource/oasst1_dense_flat\",\n pre_process = lambda ds:ds.remove_columns('labels'))\n\n\nlanguage_identification = Classification(\"text\",labels=\"labels\", dataset_name=\"papluca/language-identification\")\nwili_2018_langid = Classification(\"sentence\",labels=\"label\",dataset_name=\"wili_2018\")\n\nexams = MultipleChoice(get.question.stem, choices_list=get.question.choices.text,\n labels=lambda x:'ABCDE'.index(x['answerKey']),\n dataset_name=\"exams\", config_name='multilingual',\n pre_process=lambda ds:ds.filter(lambda x: x['answerKey'] in \"ABCDE\"))\n\nxcsr = MultipleChoice(get.question.stem, choices_list=get.question.choices.text,\n labels=lambda x:'ABCDE'.index(x['answerKey']),\n **all('xcsr'))\n\nxcopa = MultipleChoice(\"premise\",choices=['choice1','choice2'],labels=\"label\",\n **all('xcopa'))\n\n#xstory = MultipleChoice(constant(''),choices=[\"text_right_ending\",\"text_wrong_ending\"],labels=constant(0), **all(\"juletxara/xstory_cloze\"))\n\nxstory = MultipleChoice(lambda x: \"\\n\".join([x[f'input_sentence_{i}'] for i in range(1,5)]),\n choices=[\"sentence_quiz1\",\"sentence_quiz2\"],labels=constant(0), **all(\"juletxara/xstory_cloze\"))\n\n\nxglue_ner = TokenClassification(\"words\",\"ner\", dataset_name=\"xglue\",config_name=\"ner\")\nxglue_pos = TokenClassification(\"words\",\"pos\", dataset_name=\"xglue\",config_name=\"pos\")\n\n#disrpt_23 = Classification(\"unit1_sent\", \"unit2_sent\", \"label\",**all(\"metaeval/disrpt\"))\n\nudep__pos = TokenClassification('tokens','upos', **all('universal_dependencies'))\n\ndef udep_post_process(ds):\n return ds.cast_column('labels', Sequence(ClassLabel(names=udep_labels)))\n\n#udep__deprel = TokenClassification('tokens',lambda x:[udep_labels.index(a) for a in x['deprel']],\n# **all('universal_dependencies'),post_process=udep_post_process)\n\noasst_rlhf = MultipleChoice(\"prompt\",choices=['chosen','rejected'],labels=constant(0),\n dataset_name=\"tasksource/oasst1_pairwise_rlhf_reward\")\n\nsentiment = Classification(\"text\",labels=\"label\", dataset_name=\"tyqiangz/multilingual-sentiments\",config_name=\"all\",\n pre_process=lambda ds:ds.filter(lambda x: \"amazon_reviews\" not in x['source']) )\ntweet_sentiment = Classification(\"text\", labels=\"label\", **all('cardiffnlp/tweet_sentiment_multilingual'))\nreview_sentiment = Classification(\"review_body\",labels=\"stars\", dataset_name=\"amazon_reviews_multi\",config_name=\"all_languages\")\nemotion = Classification(\"text\",labels=\"emotion\",dataset_name=\"metaeval/universal-joy\")\n# in mms\n\nmms_sentiment = Classification(\"text\",labels=\"label\",dataset_name='Brand24/mms')\n\nmapa_fine = TokenClassification(\"tokens\",\"coarse_grained\",dataset_name='joelito/mapa')\nmapa_corase = TokenClassification(\"tokens\",\"fine_grained\",dataset_name='joelito/mapa')\n\naces_ranking = MultipleChoice(\"source\",choices=['good-translation','incorrect-translation'],labels=constant(0), dataset_name='nikitam/ACES')\naces_phenomena = Classification('source','incorrect-translation','phenomena', dataset_name='nikitam/ACES')\n\namazon_intent = Classification(\"utt\",labels=\"intent\",**all('AmazonScience/massive'))\n# dataset_name='glue',config_name=['ocnli','afqmc'])\n\ntidy_as2=Classification(\"Question\",\"Sentence\",\"Label\",dataset_name='tasksource/tydi-as2-balanced') \n\nmulticoner = TokenClassification(\"tokens\",\"ner_tags_index\", **all(\"MultiCoNER/multiconer_v2\"))\n\nmtop = Classification(\"question\",labels=\"intent\", dataset_name=\"tasksource/mtop\")\n\nmlabel_nli = Classification(\"premise\",\"hypothesis\",\"labels\",dataset_name=\"tasksource/multilingual-zero-shot-label-nli\")\n\n#wino_x\n# clue, klue, indic_glue\n# SMS_Spam_Multilingual_Collection_Dataset\n" }, { "path": "src/tasksource/preprocess.py", "content": "from collections.abc import Iterable\nfrom dotwiz import DotWiz\nfrom dataclasses import dataclass\nfrom typing import Union\nimport itertools\nimport funcy as fc\nimport exrex \nimport magicattr \nimport numpy as np\nimport copy\nimport datasets\nimport time\n\nMAX_MC_OPTIONS = 4\n\ndef get_column_names(dataset):\n cn = dataset.column_names\n if type(cn)==dict:\n return set(fc.flatten(cn.values()))\n else:\n return set(cn)\n\n\ndef sample_dataset(dataset,n=10000, n_eval=1000,seed=0):\n for k in dataset:\n n_k=(n if k=='train' else n_eval)\n if n_k and len(dataset[k])>n_k:\n dataset[k]=dataset[k].train_test_split(train_size=n_k,seed=seed)['train']\n return dataset\n\nclass Preprocessing(DotWiz):\n default_splits = ('train','validation','test')\n _instances = []\n\n def __post_init__(self):\n Preprocessing._instances+=[self]\n\n @staticmethod\n def __map_to_target(x,fn=lambda x:None, target=None):\n x[target]=fn(x)\n return x\n \n def load(self):\n return self(datasets.load_dataset(self.dataset_name,self.config_name))\n\n def __call__(self,dataset, max_rows=None, max_rows_eval=None,seed=0):\n dataset = self.pre_process(dataset)\n\n # manage splits\n for k,v in zip(self.default_splits, self.splits):\n if v and k!=v:\n dataset[k]=dataset[v]\n del dataset[v]\n if k in dataset and not v: # obfuscated label\n del dataset[k]\n dataset = fix_splits(dataset)\n\n for k in list(dataset.keys()):\n if k not in self.default_splits:\n del dataset[k]\n dataset = sample_dataset(dataset, max_rows, max_rows_eval,seed=seed)\n \n # field annotated with a string\n substitutions = {v:k for k,v in self.to_dict().items()\n if (k and k not in {'splits','dataset_name','config_name'} \n and type(v)==str and k!=v)}\n\n dataset=dataset.remove_columns([c for c in substitutions.values() if c in dataset['train'].features and c not in substitutions])\n dataset=dataset.rename_columns(substitutions)\n\n # field annotated with a function \n for k in self.to_dict().keys():\n v=getattr(self, k)\n if callable(v) and k not in {\"post_process\",\"pre_process\",\"load\"}:\n dataset=dataset.map(self.__map_to_target,\n fn_kwargs={'fn':v,'target':k})\n\n dataset=dataset.remove_columns(\n get_column_names(dataset)-set(self.to_dict().keys()))\n dataset = fix_labels(dataset)\n dataset = fix_splits(dataset) # again: label mapping changed\n dataset = self.post_process(dataset)\n return dataset\n\n\n@dataclass\nclass cat(Preprocessing):\n fields:Union[str,list]=None\n separator:str=' '\n \n def __call__(self, example=None):\n y=[np.char.array(example[f]) + sep \n for f,sep in zip(self.fields[::-1],itertools.repeat(self.separator))]\n y=list(sum(*y))\n if len(y)==1:\n y=y[0]\n return y\n\n\ndef pretty(f):\n class pretty_f(DotWiz):\n def __init__(self,*args):\n self.__f_arg = f(*args)\n for a in args:\n setattr(self,'value',a)\n \n def __call__(self, *args,**kwargs):\n return self.__f_arg(*args,**kwargs)\n\n def __repr__(self):\n return f\"{self.__f_arg.__qualname__ .split('.')[0]}({self.value})\"\n return pretty_f\n\nclass dotgetter:\n def __init__(self, path=''):\n self.path=path\n\n def __bool__(self):\n return bool(self.path)\n\n def __getattr__(self, k):\n return self.__class__(f'{self.path}.{k}'.lstrip('.'))\n \n def __getitem__(self, i):\n return self.__class__(f'{self.path}[{i}]')\n\n def __call__(self, example=None):\n return magicattr.get(DotWiz(example), self.path)\n\n def __hash__(self):\n return hash(self.path)\n\n\n@dataclass\nclass ClassificationFields(Preprocessing):\n sentence1:str='sentence1'\n sentence2:str='sentence2'\n labels:str='labels'\n\n@dataclass\nclass Seq2SeqLMFields(Preprocessing):\n prompt:str='prompt'\n output:str='output'\n\n@dataclass\nclass TokenClassificationFields(Preprocessing):\n tokens:str='tokens'\n labels:str='labels'\n \n@dataclass\nclass MultipleChoiceFields(Preprocessing):\n inputs:str='input'\n choices:Iterable=tuple()\n labels:str='labels'\n choices_list:str=None\n def __post_init__(self):\n for i, c in enumerate(self.choices):\n setattr(self,f'choice{i}',c)\n delattr(self,'choices')\n if not self.choices_list:\n delattr(self,'choices_list')\n \n def __call__(self,dataset, *args, **kwargs):\n dataset = super().__call__(dataset, *args, **kwargs)\n if self.choices_list:\n dataset = dataset.filter(lambda x: 10),\n dataset_name=\"openai/webgpt_comparisons\")\n\nsynthetic_instruct = MultipleChoice('prompt', choices=['chosen', 'rejected'],\n labels=constant(0), dataset_name=\"Dahoas/synthetic-instruct-gptj-pairwise\")\n\nscruples = Classification(\"text\",labels=\"binarized_label\",dataset_name=\"metaeval/scruples\")\n\nwouldyourather = MultipleChoice(constant('Most people would rather:'), choices=['option_a','option_b'],\n labels= lambda x: int(x['votes_a']