Update files from the datasets library (from 1.16.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.16.0

dataset_infos.json

@@ -1 +1 @@
-{"ecthr_a": {"description": "The European Court of Human Rights (ECtHR) hears allegations that a state has \nbreached human rights provisions of the European Convention of Human Rights (ECHR). \nThe dataset contains approx. 11K cases from the ECtHR public database. \nThe cases are chronologically split into training (9k, 2001--2016), \ndevelopment (1k, 2016--2017), and test (1k, 2017--2019). \nFor each case, the dataset provides a list of factual paragraphs (facts) from the case description. \nEach case is mapped to articles of the ECHR that were violated (if any).", "citation": "@inproceedings{chalkidis-etal-2021-paragraph,\n    title = \"Paragraph-level Rationale Extraction through Regularization: A case study on {E}uropean Court of Human Rights Cases\",\n    author = \"Chalkidis, Ilias  and\n      Fergadiotis, Manos  and\n      Tsarapatsanis, Dimitrios  and\n      Aletras, Nikolaos  and\n      Androutsopoulos, Ion  and\n      Malakasiotis, Prodromos\",\n    booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n    month = jun,\n    year = \"2021\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.naacl-main.22\",\n    doi = \"10.18653/v1/2021.naacl-main.22\",\n    pages = \"226--241\",\n}\n}\n@misc{chalkidis-etal-2021-lexglue,\n      title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, \n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={xxxx.xxxxx},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "https://archive.org/details/ECtHR-NAACL2021", "license": "", "features": {"text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"num_classes": 10, "names": ["2", "3", "5", "6", "8", "9", "10", "11", "14", "P1-1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "ecthr_a", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 89637461, "num_examples": 9000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 11884180, "num_examples": 1000, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 10985180, "num_examples": 1000, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/ecthr.tar.gz": {"num_bytes": 32852475, "checksum": "461c1f6016af3a7ac0bd115c1f9ff65031258bfec39e570fec74a16d8946398e"}}, "download_size": 32852475, "post_processing_size": null, "dataset_size": 112506821, "size_in_bytes": 145359296}, "ecthr_b": {"description": "The European Court of Human Rights (ECtHR) hears allegations that a state has \nbreached human rights provisions of the European Convention of Human Rights (ECHR). \nFor each case, the dataset provides a list of factual paragraphs (facts) from the case description. \nEach case is mapped to articles of ECHR that were allegedly violated (considered by the court).", "citation": "@inproceedings{chalkidis-etal-2021-paragraph,\n    title = \"Paragraph-level Rationale Extraction through Regularization: A case study on {E}uropean Court of Human Rights Cases\",\n    author = \"Chalkidis, Ilias  \n    and Fergadiotis, Manos  \n    and Tsarapatsanis, Dimitrios  \n    and  Aletras, Nikolaos  \n    and Androutsopoulos, Ion  \n    and Malakasiotis, Prodromos\",\n    booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n    year = \"2021\",\n    address = \"Online\",\n    url = \"https://aclanthology.org/2021.naacl-main.22\",\n}\n}\n@misc{chalkidis-etal-2021-lexglue,\n      title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, \n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={xxxx.xxxxx},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "https://archive.org/details/ECtHR-NAACL2021", "license": "", "features": {"text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"num_classes": 10, "names": ["2", "3", "5", "6", "8", "9", "10", "11", "14", "P1-1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "ecthr_b", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 89657661, "num_examples": 9000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 11886940, "num_examples": 1000, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 10987828, "num_examples": 1000, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/ecthr.tar.gz": {"num_bytes": 32852475, "checksum": "461c1f6016af3a7ac0bd115c1f9ff65031258bfec39e570fec74a16d8946398e"}}, "download_size": 32852475, "post_processing_size": null, "dataset_size": 112532429, "size_in_bytes": 145384904}, "eurlex": {"description": "European Union (EU) legislation is published in EUR-Lex portal.\nAll EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, \na multilingual thesaurus maintained by the Publications Office. \nThe current version of EuroVoc contains more than 7k concepts referring to various activities \nof the EU and its Member States (e.g., economics, health-care, trade). \nGiven a document, the task is to predict its EuroVoc labels (concepts).", "citation": "@inproceedings{chalkidis-etal-2021-multieurlex,\n  author = {Chalkidis, Ilias and \n  Fergadiotis, Manos and \n  Androutsopoulos, Ion},\n  title = {MultiEURLEX -- A multi-lingual and multi-label legal document\n               classification dataset for zero-shot cross-lingual transfer},\n  booktitle = {Proceedings of the 2021 Conference on Empirical Methods\n               in Natural Language Processing},\n  year = {2021},\n  location = {Punta Cana, Dominican Republic},\n}\n}\n@misc{chalkidis-etal-2021-lexglue,\n      title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, \n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={xxxx.xxxxx},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "https://zenodo.org/record/5363165#.YVJOAi8RqaA", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"feature": {"num_classes": 127, "names": ["100163", "100164", "100165", "100166", "100167", "100168", "100169", "100170", "100171", "100172", "100173", "100174", "100175", "100176", "100177", "100178", "100179", "100180", "100181", "100182", "100183", "100184", "100185", "100186", "100187", "100188", "100189", "100190", "100191", "100192", "100193", "100194", "100195", "100196", "100197", "100198", "100199", "100200", "100201", "100202", 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"supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "eurlex", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 390789505, "num_examples": 55000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 59742502, "num_examples": 5000, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 41546764, "num_examples": 5000, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/eurlex.tar.gz": {"num_bytes": 125413277, "checksum": "82376ff55c3812632d8a21ad0d7e515e2e7ec6431ca7673a454cdd41a3a7bf46"}}, "download_size": 125413277, "post_processing_size": null, "dataset_size": 492078771, "size_in_bytes": 617492048}, "scotus": {"description": "The US Supreme Court  (SCOTUS) is the highest federal court in the United States of America \nand generally hears only the most controversial or otherwise complex cases which have not \nbeen sufficiently well solved by lower courts. This is a single-label multi-class classification \ntask, where given a document (court opinion), the task is to predict the relevant issue areas.\nThe 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute).", "citation": "@misc{spaeth2020,\n author = {Harold J. Spaeth and Lee Epstein and Andrew D. Martin, Jeffrey A. Segal \n and Theodore J. Ruger and Sara C. Benesh},\n year = {2020},\n title ={{Supreme Court Database, Version 2020 Release 01}},\n url= {http://Supremecourtdatabase.org},\n howpublished={Washington University Law}\n } \n}\n@misc{chalkidis-etal-2021-lexglue,\n      title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, \n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={xxxx.xxxxx},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "http://scdb.wustl.edu/data.php", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 13, "names": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "scotus", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 178959320, "num_examples": 5000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 76213283, "num_examples": 1400, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 75600247, "num_examples": 1400, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/scotus.tar.gz": {"num_bytes": 104763335, "checksum": "d53cc99aaf60b24ca7e4cf634f08a2572b5b3532f83aecdfc2c4257050dc9d0a"}}, "download_size": 104763335, "post_processing_size": null, "dataset_size": 330772850, "size_in_bytes": 435536185}, "ledgar": {"description": "LEDGAR dataset aims contract provision (paragraph) classification. \nThe contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) \nfilings, which are publicly available from EDGAR. Each label represents the single main topic \n(theme) of the corresponding contract provision.", "citation": "@inproceedings{tuggener-etal-2020-ledgar,\n    title = \"{LEDGAR}: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts\",\n    author = {Tuggener, Don  and\n      von D{\"a}niken, Pius  and\n      Peetz, Thomas  and\n      Cieliebak, Mark},\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    year = \"2020\",\n    address = \"Marseille, France\",\n    url = \"https://aclanthology.org/2020.lrec-1.155\",\n}\n}\n@misc{chalkidis-etal-2021-lexglue,\n      title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, \n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={xxxx.xxxxx},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "https://metatext.io/datasets/ledgar", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 100, "names": ["Adjustments", "Agreements", "Amendments", "Anti-Corruption Laws", "Applicable Laws", "Approvals", "Arbitration", "Assignments", "Assigns", "Authority", "Authorizations", "Base Salary", "Benefits", "Binding Effects", "Books", "Brokers", "Capitalization", "Change In Control", "Closings", "Compliance With Laws", "Confidentiality", "Consent To Jurisdiction", "Consents", "Construction", "Cooperation", "Costs", "Counterparts", "Death", "Defined Terms", "Definitions", "Disability", "Disclosures", "Duties", "Effective Dates", "Effectiveness", "Employment", "Enforceability", "Enforcements", "Entire Agreements", "Erisa", "Existence", "Expenses", "Fees", "Financial Statements", "Forfeitures", "Further Assurances", "General", "Governing Laws", "Headings", "Indemnifications", "Indemnity", "Insurances", "Integration", "Intellectual Property", "Interests", "Interpretations", "Jurisdictions", "Liens", "Litigations", "Miscellaneous", "Modifications", "No Conflicts", "No Defaults", "No Waivers", "Non-Disparagement", "Notices", "Organizations", "Participations", "Payments", "Positions", "Powers", "Publicity", "Qualifications", "Records", "Releases", "Remedies", "Representations", "Sales", "Sanctions", "Severability", "Solvency", "Specific Performance", "Submission To Jurisdiction", "Subsidiaries", "Successors", "Survival", "Tax Withholdings", "Taxes", "Terminations", "Terms", "Titles", "Transactions With Affiliates", "Use Of Proceeds", "Vacations", "Venues", "Vesting", "Waiver Of Jury Trials", "Waivers", "Warranties", "Withholdings"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "ledgar", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 43358315, "num_examples": 60000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 6845585, "num_examples": 10000, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 7143592, "num_examples": 10000, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/ledgar.tar.gz": {"num_bytes": 16255623, "checksum": "f7507bcce46ce03e3e91b8aaa1b84ddf6e8f1d628c0d7fa351f97ce45366d5d8"}}, "download_size": 16255623, "post_processing_size": null, "dataset_size": 57347492, "size_in_bytes": 73603115}, "unfair_tos": {"description": "The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, \nEbay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of \nunfair contractual terms (sentences), meaning terms that potentially violate user rights \naccording to the European consumer law.", "citation": "@article{lippi-etal-2019-claudette,\n    title = \"{CLAUDETTE}: an automated detector of potentially unfair clauses in online terms of service\",\n    author = {Lippi, Marco\n    and Pa\u0142ka, Przemys\u0142aw\n    and Contissa, Giuseppe\n    and Lagioia, Francesca\n    and Micklitz, Hans-Wolfgang\n    and Sartor, Giovanni\n    and Torroni, Paolo},\n    journal = \"Artificial Intelligence and Law\",\n    year = \"2019\",\n    publisher = \"Springer\",\n    url = \"https://doi.org/10.1007/s10506-019-09243-2\",\n    pages = \"117--139\",\n}\n@misc{chalkidis-etal-2021-lexglue,\n      title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, \n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={xxxx.xxxxx},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "http://claudette.eui.eu", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"feature": {"num_classes": 8, "names": ["Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "unfair_tos", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1041790, "num_examples": 5532, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 303107, "num_examples": 1607, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 452119, "num_examples": 2275, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/unfair_tos.tar.gz": {"num_bytes": 511342, "checksum": "934470d74b62139dfbfad4a13b75a32e4a4d26a680ab12eedfb7659cdf669d53"}}, "download_size": 511342, "post_processing_size": null, "dataset_size": 1797016, "size_in_bytes": 2308358}, "case_hold": {"description": "The CaseHOLD (Case Holdings on Legal Decisions) dataset contains approx. 53k multiple choice \nquestions about holdings of US court cases from the Harvard Law Library case law corpus. \nHoldings are short summaries of legal rulings accompany referenced decisions relevant for the present case.\nThe input consists of an excerpt (or prompt) from a court decision, containing a reference \nto a particular case, while the holding statement is masked out. The model must identify \nthe correct (masked) holding statement from a selection of five choices.", "citation": "@inproceedings{Zheng2021,\n  author    = {Lucia Zheng and\n               Neel Guha and\n               Brandon R. Anderson and\n               Peter Henderson and\n               Daniel E. Ho},\n  title     = {When Does Pretraining Help? Assessing Self-Supervised Learning for\n               Law and the CaseHOLD Dataset},\n  year      = {2021},\n  booktitle = {International Conference on Artificial Intelligence and Law},\n}\n@misc{chalkidis-etal-2021-lexglue,\n      title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, \n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={xxxx.xxxxx},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "https://github.com/reglab/casehold", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "contexts": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "endings": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "label": {"num_classes": 5, "names": ["0", "1", "2", "3", "4"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "case_hold", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 229240778, "num_examples": 45000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 18350872, "num_examples": 3600, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 19860783, "num_examples": 3900, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/casehold.tar.gz": {"num_bytes": 30422703, "checksum": "728827dae0019880fe6be609e23f8c47fa2b49a2f0814a36687ace8db1c32d5e"}}, "download_size": 30422703, "post_processing_size": null, "dataset_size": 267452433, "size_in_bytes": 297875136}}
+{"ecthr_a": {"description": "The European Court of Human Rights (ECtHR) hears allegations that a state has\nbreached human rights provisions of the European Convention of Human Rights (ECHR).\nFor each case, the dataset provides a list of factual paragraphs (facts) from the case description.\nEach case is mapped to articles of the ECHR that were violated (if any).", "citation": "@inproceedings{chalkidis-etal-2021-paragraph,\n    title = \"Paragraph-level Rationale Extraction through Regularization: A case study on {E}uropean Court of Human Rights Cases\",\n    author = \"Chalkidis, Ilias  and\n      Fergadiotis, Manos  and\n      Tsarapatsanis, Dimitrios  and\n      Aletras, Nikolaos  and\n      Androutsopoulos, Ion  and\n      Malakasiotis, Prodromos\",\n    booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n    month = jun,\n    year = \"2021\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.naacl-main.22\",\n    doi = \"10.18653/v1/2021.naacl-main.22\",\n    pages = \"226--241\",\n}\n}\n@article{chalkidis-etal-2021-lexglue,\n      title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},\n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={2110.00976},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      note = {arXiv: 2110.00976},\n}", "homepage": "https://archive.org/details/ECtHR-NAACL2021", "license": "", "features": {"text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"num_classes": 10, "names": ["2", "3", "5", "6", "8", "9", "10", "11", "14", "P1-1"], 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Rights (ECtHR) hears allegations that a state has\nbreached human rights provisions of the European Convention of Human Rights (ECHR).\nFor each case, the dataset provides a list of factual paragraphs (facts) from the case description.\nEach case is mapped to articles of ECHR that were allegedly violated (considered by the court).", "citation": "@inproceedings{chalkidis-etal-2021-paragraph,\n    title = \"Paragraph-level Rationale Extraction through Regularization: A case study on {E}uropean Court of Human Rights Cases\",\n    author = \"Chalkidis, Ilias\n    and Fergadiotis, Manos\n    and Tsarapatsanis, Dimitrios\n    and  Aletras, Nikolaos\n    and Androutsopoulos, Ion\n    and Malakasiotis, Prodromos\",\n    booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n    year = \"2021\",\n    address = \"Online\",\n    url = \"https://aclanthology.org/2021.naacl-main.22\",\n}\n}\n@article{chalkidis-etal-2021-lexglue,\n      title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},\n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={2110.00976},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      note = {arXiv: 2110.00976},\n}", "homepage": "https://archive.org/details/ECtHR-NAACL2021", "license": "", "features": {"text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"num_classes": 10, "names": ["2", "3", "5", "6", "8", "9", "10", "11", "14", "P1-1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "ecthr_b", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 89657661, "num_examples": 9000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 11886940, "num_examples": 1000, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 10987828, "num_examples": 1000, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/ecthr.tar.gz": {"num_bytes": 32852475, "checksum": "461c1f6016af3a7ac0bd115c1f9ff65031258bfec39e570fec74a16d8946398e"}}, "download_size": 32852475, "post_processing_size": null, "dataset_size": 112532429, "size_in_bytes": 145384904}, "eurlex": {"description": "European Union (EU) legislation is published in EUR-Lex portal.\nAll EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus,\na multilingual thesaurus maintained by the Publications Office.\nThe current version of EuroVoc contains more than 7k concepts referring to various activities\nof the EU and its Member States (e.g., economics, health-care, trade).\nGiven a document, the task is to predict its EuroVoc labels (concepts).", "citation": "@inproceedings{chalkidis-etal-2021-multieurlex,\n  author = {Chalkidis, Ilias and\n  Fergadiotis, Manos and\n  Androutsopoulos, Ion},\n  title = {MultiEURLEX -- A multi-lingual and multi-label legal document\n               classification dataset for zero-shot cross-lingual transfer},\n  booktitle = {Proceedings of the 2021 Conference on Empirical Methods\n               in Natural Language Processing},\n  year = {2021},\n  location = {Punta Cana, Dominican Republic},\n}\n}\n@article{chalkidis-etal-2021-lexglue,\n      title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},\n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={2110.00976},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      note = {arXiv: 2110.00976},\n}", "homepage": "https://zenodo.org/record/5363165#.YVJOAi8RqaA", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"feature": {"num_classes": 127, "names": ["100163", "100164", "100165", "100166", "100167", "100168", "100169", "100170", "100171", "100172", "100173", "100174", "100175", "100176", "100177", "100178", "100179", "100180", "100181", "100182", "100183", "100184", "100185", "100186", "100187", "100188", "100189", "100190", "100191", "100192", "100193", "100194", "100195", "100196", "100197", "100198", "100199", "100200", "100201", "100202", "100203", "100204", "100205", "100206", "100207", "100208", "100209", "100210", "100211", "100212", "100213", "100214", "100215", "100216", "100217", "100218", "100219", "100220", "100221", "100222", "100223", "100224", "100225", "100226", "100227", "100228", "100229", "100230", "100231", "100232", "100233", "100234", "100235", "100236", "100237", "100238", "100239", "100240", "100241", "100242", "100243", "100244", "100245", "100246", "100247", "100248", "100249", "100250", "100251", "100252", "100253", "100254", "100255", "100256", "100257", "100258", "100259", "100260", "100261", "100262", "100263", "100264", "100265", "100266", "100267", "100268", "100269", "100270", "100271", "100272", "100273", "100274", "100275", "100276", "100277", "100278", "100279", "100280", "100281", "100282", "100283", "100284", "100285", "100286", "100287", "100288", "100289"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "eurlex", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 390789505, "num_examples": 55000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 59742502, "num_examples": 5000, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 41546764, "num_examples": 5000, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/eurlex.tar.gz": {"num_bytes": 125413277, "checksum": "82376ff55c3812632d8a21ad0d7e515e2e7ec6431ca7673a454cdd41a3a7bf46"}}, "download_size": 125413277, "post_processing_size": null, "dataset_size": 492078771, "size_in_bytes": 617492048}, "scotus": {"description": "The US Supreme Court  (SCOTUS) is the highest federal court in the United States of America\nand generally hears only the most controversial or otherwise complex cases which have not\nbeen sufficiently well solved by lower courts. This is a single-label multi-class classification\ntask, where given a document (court opinion), the task is to predict the relevant issue areas.\nThe 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute).", "citation": "@misc{spaeth2020,\n author = {Harold J. Spaeth and Lee Epstein and Andrew D. Martin, Jeffrey A. Segal\n and Theodore J. Ruger and Sara C. Benesh},\n year = {2020},\n title ={{Supreme Court Database, Version 2020 Release 01}},\n url= {http://Supremecourtdatabase.org},\n howpublished={Washington University Law}\n}\n@article{chalkidis-etal-2021-lexglue,\n      title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},\n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={2110.00976},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      note = {arXiv: 2110.00976},\n}", "homepage": "http://scdb.wustl.edu/data.php", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 13, "names": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "scotus", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 178959320, "num_examples": 5000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 76213283, "num_examples": 1400, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 75600247, "num_examples": 1400, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/scotus.tar.gz": {"num_bytes": 104763335, "checksum": "d53cc99aaf60b24ca7e4cf634f08a2572b5b3532f83aecdfc2c4257050dc9d0a"}}, "download_size": 104763335, "post_processing_size": null, "dataset_size": 330772850, "size_in_bytes": 435536185}, "ledgar": {"description": "LEDGAR dataset aims contract provision (paragraph) classification.\nThe contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC)\nfilings, which are publicly available from EDGAR. Each label represents the single main topic\n(theme) of the corresponding contract provision.", "citation": "@inproceedings{tuggener-etal-2020-ledgar,\n    title = \"{LEDGAR}: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts\",\n    author = {Tuggener, Don  and\n      von D{\"a}niken, Pius  and\n      Peetz, Thomas  and\n      Cieliebak, Mark},\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    year = \"2020\",\n    address = \"Marseille, France\",\n    url = \"https://aclanthology.org/2020.lrec-1.155\",\n}\n}\n@article{chalkidis-etal-2021-lexglue,\n      title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},\n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={2110.00976},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      note = {arXiv: 2110.00976},\n}", "homepage": "https://metatext.io/datasets/ledgar", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 100, "names": ["Adjustments", "Agreements", "Amendments", "Anti-Corruption Laws", "Applicable Laws", "Approvals", "Arbitration", "Assignments", "Assigns", "Authority", "Authorizations", "Base Salary", "Benefits", "Binding Effects", "Books", "Brokers", "Capitalization", "Change In Control", "Closings", "Compliance With Laws", "Confidentiality", "Consent To Jurisdiction", "Consents", "Construction", "Cooperation", "Costs", "Counterparts", "Death", "Defined Terms", "Definitions", "Disability", "Disclosures", "Duties", "Effective Dates", "Effectiveness", "Employment", "Enforceability", "Enforcements", "Entire Agreements", "Erisa", "Existence", "Expenses", "Fees", "Financial Statements", "Forfeitures", "Further Assurances", "General", "Governing Laws", "Headings", "Indemnifications", "Indemnity", "Insurances", "Integration", "Intellectual Property", "Interests", "Interpretations", "Jurisdictions", "Liens", "Litigations", "Miscellaneous", "Modifications", "No Conflicts", "No Defaults", "No Waivers", "Non-Disparagement", "Notices", "Organizations", "Participations", "Payments", "Positions", "Powers", "Publicity", "Qualifications", "Records", "Releases", "Remedies", "Representations", "Sales", "Sanctions", "Severability", "Solvency", "Specific Performance", "Submission To Jurisdiction", "Subsidiaries", "Successors", "Survival", "Tax Withholdings", "Taxes", "Terminations", "Terms", "Titles", "Transactions With Affiliates", "Use Of Proceeds", "Vacations", "Venues", "Vesting", "Waiver Of Jury Trials", "Waivers", "Warranties", "Withholdings"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "ledgar", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 43358315, "num_examples": 60000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 6845585, "num_examples": 10000, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 7143592, "num_examples": 10000, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/ledgar.tar.gz": {"num_bytes": 16255623, "checksum": "f7507bcce46ce03e3e91b8aaa1b84ddf6e8f1d628c0d7fa351f97ce45366d5d8"}}, "download_size": 16255623, "post_processing_size": null, "dataset_size": 57347492, "size_in_bytes": 73603115}, "unfair_tos": {"description": "The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube,\nEbay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of\nunfair contractual terms (sentences), meaning terms that potentially violate user rights\naccording to the European consumer law.", "citation": "@article{lippi-etal-2019-claudette,\n    title = \"{CLAUDETTE}: an automated detector of potentially unfair clauses in online terms of service\",\n    author = {Lippi, Marco\n    and Pa\u0142ka, Przemys\u0142aw\n    and Contissa, Giuseppe\n    and Lagioia, Francesca\n    and Micklitz, Hans-Wolfgang\n    and Sartor, Giovanni\n    and Torroni, Paolo},\n    journal = \"Artificial Intelligence and Law\",\n    year = \"2019\",\n    publisher = \"Springer\",\n    url = \"https://doi.org/10.1007/s10506-019-09243-2\",\n    pages = \"117--139\",\n}\n@article{chalkidis-etal-2021-lexglue,\n      title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},\n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={2110.00976},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      note = {arXiv: 2110.00976},\n}", "homepage": "http://claudette.eui.eu", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"feature": {"num_classes": 8, "names": ["Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "unfair_tos", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1041790, "num_examples": 5532, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 303107, "num_examples": 1607, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 452119, "num_examples": 2275, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/unfair_tos.tar.gz": {"num_bytes": 511342, "checksum": "934470d74b62139dfbfad4a13b75a32e4a4d26a680ab12eedfb7659cdf669d53"}}, "download_size": 511342, "post_processing_size": null, "dataset_size": 1797016, "size_in_bytes": 2308358}, "case_hold": {"description": "The CaseHOLD (Case Holdings on Legal Decisions) dataset contains approx. 53k multiple choice\nquestions about holdings of US court cases from the Harvard Law Library case law corpus.\nHoldings are short summaries of legal rulings accompany referenced decisions relevant for the present case.\nThe input consists of an excerpt (or prompt) from a court decision, containing a reference\nto a particular case, while the holding statement is masked out. The model must identify\nthe correct (masked) holding statement from a selection of five choices.", "citation": "@inproceedings{Zheng2021,\n  author    = {Lucia Zheng and\n               Neel Guha and\n               Brandon R. Anderson and\n               Peter Henderson and\n               Daniel E. Ho},\n  title     = {When Does Pretraining Help? Assessing Self-Supervised Learning for\n               Law and the CaseHOLD Dataset},\n  year      = {2021},\n  booktitle = {International Conference on Artificial Intelligence and Law},\n}\n@article{chalkidis-etal-2021-lexglue,\n      title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English},\n      author={Chalkidis, Ilias and\n      Jana, Abhik and\n      Hartung, Dirk and\n      Bommarito, Michael and\n      Androutsopoulos, Ion and\n      Katz, Daniel Martin and\n      Aletras, Nikolaos},\n      year={2021},\n      eprint={2110.00976},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      note = {arXiv: 2110.00976},\n}", "homepage": "https://github.com/reglab/casehold", "license": "", "features": {"context": {"dtype": "string", "id": null, "_type": "Value"}, "endings": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "label": {"num_classes": 5, "names": ["0", "1", "2", "3", "4"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lex_glue", "config_name": "case_hold", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 74781766, "num_examples": 45000, "dataset_name": "lex_glue"}, "test": {"name": "test", "num_bytes": 5989964, "num_examples": 3600, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 6474615, "num_examples": 3900, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/casehold.tar.gz": {"num_bytes": 30422703, "checksum": "728827dae0019880fe6be609e23f8c47fa2b49a2f0814a36687ace8db1c32d5e"}}, "download_size": 30422703, "post_processing_size": null, "dataset_size": 87246345, "size_in_bytes": 117669048}}

lex_glue.py

@@ -16,7 +16,6 @@
 
 import csv
 import json
-import os
 import textwrap
 
 import datasets
@@ -593,8 +592,7 @@ class LexGLUE(datasets.GeneratorBasedBuilder):
     def _info(self):
         if self.config.name == "case_hold":
             features = {
-                "question": datasets.Value("string"),
-                "contexts": datasets.features.Sequence(datasets.Value("string")),
+                "context": datasets.Value("string"),
                 "endings": datasets.features.Sequence(datasets.Value("string")),
             }
         elif "ecthr" in self.config.name:
@@ -613,60 +611,76 @@ class LexGLUE(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(self.config.data_url)
+        archive = dl_manager.download(self.config.data_url)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, self.config.data_file), "split": "train"},
+                gen_kwargs={
+                    "filepath": self.config.data_file,
+                    "split": "train",
+                    "files": dl_manager.iter_archive(archive),
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, self.config.data_file), "split": "test"},
+                gen_kwargs={
+                    "filepath": self.config.data_file,
+                    "split": "test",
+                    "files": dl_manager.iter_archive(archive),
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, self.config.data_file),
+                    "filepath": self.config.data_file,
                     "split": self.config.dev_column,
+                    "files": dl_manager.iter_archive(archive),
                 },
             ),
         ]
 
-    def _generate_examples(self, filepath, split):
+    def _generate_examples(self, filepath, split, files):
         """This function returns the examples in the raw (text) form."""
         if self.config.name == "case_hold":
             if "dummy" in filepath:
                 SPLIT_RANGES = {"train": (1, 3), "dev": (3, 5), "test": (5, 7)}
             else:
                 SPLIT_RANGES = {"train": (1, 45001), "dev": (45001, 48901), "test": (48901, 52501)}
-            with open(filepath, "r", encoding="utf-8") as f:
-                for id_, row in enumerate(list(csv.reader(f))[SPLIT_RANGES[split][0] : SPLIT_RANGES[split][1]]):
-                    yield id_, {
-                        "context": row[1],
-                        "holdings": [row[2], row[3], row[4], row[5], row[6]],
-                        "label": str(row[12]),
-                    }
-        elif self.config.multi_label:
-            with open(filepath, "r", encoding="utf-8") as f:
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    labels = sorted(
-                        list(set(data[self.config.label_column]).intersection(set(self.config.label_classes)))
-                    )
-                    if data["data_type"] == split:
+            for path, f in files:
+                if path == filepath:
+                    f = (line.decode("utf-8") for line in f)
+                    for id_, row in enumerate(list(csv.reader(f))[SPLIT_RANGES[split][0] : SPLIT_RANGES[split][1]]):
                         yield id_, {
-                            "text": data[self.config.text_column],
-                            "labels": labels,
+                            "context": row[1],
+                            "endings": [row[2], row[3], row[4], row[5], row[6]],
+                            "label": str(row[12]),
                         }
+                    break
+        elif self.config.multi_label:
+            for path, f in files:
+                if path == filepath:
+                    for id_, row in enumerate(f):
+                        data = json.loads(row.decode("utf-8"))
+                        labels = sorted(
+                            list(set(data[self.config.label_column]).intersection(set(self.config.label_classes)))
+                        )
+                        if data["data_type"] == split:
+                            yield id_, {
+                                "text": data[self.config.text_column],
+                                "labels": labels,
+                            }
+                    break
         else:
-            with open(filepath, "r", encoding="utf-8") as f:
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    if data["data_type"] == split:
-                        yield id_, {
-                            "text": data[self.config.text_column],
-                            "label": data[self.config.label_column],
-                        }
+            for path, f in files:
+                if path == filepath:
+                    for id_, row in enumerate(f):
+                        data = json.loads(row.decode("utf-8"))
+                        if data["data_type"] == split:
+                            yield id_, {
+                                "text": data[self.config.text_column],
+                                "label": data[self.config.label_column],
+                            }
+                    break