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{"ecthr": {"description": "The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights\nprovisions of the European Convention of Human Rights (ECHR). We use the dataset of Chalkidis et al.\n(2021), which contains 11K cases from ECtHR's public database. Each case is mapped to articles of the ECHR\nthat were violated (if any). This is a multi-label text classification task. Given the facts of a case,\nthe goal is to predict the ECHR articles that were violated, if any, as decided (ruled) 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 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@inproceedings{chalkidis-etal-2022-fairlex,\n author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and\n Tomada, Letizia and Schwemer, Sebastian Felix and S\u00f8gaard, Anders},\n title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},\n booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},\n year={2022},\n address={Dublin, Ireland}\n}\n", "homepage": "https://huggingface.co/datasets/ecthr_cases", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "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"}, "applicant_age": {"num_classes": 4, "names": ["n/a", "<=35", "<=65", ">65"], "names_file": null, "id": null, "_type": "ClassLabel"}, "applicant_gender": {"num_classes": 3, "names": ["n/a", "male", "female"], "names_file": null, "id": null, "_type": "ClassLabel"}, "defendant_state": {"num_classes": 2, "names": ["C.E. European", "Rest of Europe"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fairlex", "config_name": "ecthr", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 90225213, "num_examples": 9000, "dataset_name": "fairlex"}, "test": {"name": "test", "num_bytes": 11952744, "num_examples": 1000, "dataset_name": "fairlex"}, "validation": {"name": "validation", "num_bytes": 11052012, "num_examples": 1000, "dataset_name": "fairlex"}}, "download_checksums": {"https://zenodo.org/record/6322643/files/ecthr.zip": {"num_bytes": 31922049, "checksum": "4b53b267f668ed31edc74575a484bb0ba1fb2fba2dfab30b45e77d202f575dfa"}}, "download_size": 31922049, "post_processing_size": null, "dataset_size": 113229969, "size_in_bytes": 145152018}, "scotus": {"description": "The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally\nhears only the most controversial or otherwise complex cases which have not been sufficiently well solved\nby lower courts. We combine information from SCOTUS opinions with the Supreme Court DataBase (SCDB)\n(Spaeth, 2020). SCDB provides metadata (e.g., date of publication, decisions, issues, decision directions\nand many more) for all cases. We consider the available 14 thematic issue areas (e.g, Criminal Procedure,\nCivil Rights, Economic Activity, etc.). This is a single-label multi-class document classification task.\nGiven the court opinion, the goal is to predict the issue area whose focus is on the subject matter\nof 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@inproceedings{chalkidis-etal-2022-fairlex,\n author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and\n Tomada, Letizia and Schwemer, Sebastian Felix and S\u00f8gaard, Anders},\n title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},\n booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},\n year={2022},\n address={Dublin, Ireland}\n}\n", "homepage": "http://scdb.wustl.edu/data.php", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 11, "names": ["Criminal Procedure", "Civil Rights", "First Amendment", "Due Process", "Privacy", "Attorneys", "Unions", "Economic Activity", "Judicial Power", "Federalism", "Federal Taxation"], "names_file": null, "id": null, "_type": "ClassLabel"}, "decision_direction": {"num_classes": 2, "names": ["conservative", "liberal"], "names_file": null, "id": null, "_type": "ClassLabel"}, "respondent_type": {"num_classes": 5, "names": ["other", "person", "organization", "public entity", "facility"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fairlex", "config_name": "scotus", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 350101125, "num_examples": 7417, "dataset_name": "fairlex"}, "test": {"name": "test", "num_bytes": 41482797, "num_examples": 931, "dataset_name": "fairlex"}, "validation": {"name": "validation", "num_bytes": 43593809, "num_examples": 914, "dataset_name": "fairlex"}}, "download_checksums": {"https://zenodo.org/record/6322643/files/scotus.zip": {"num_bytes": 134767984, "checksum": "e3b6154069137816049be127189fe649a1251f24fab5cafab7aa345733bdde3c"}}, "download_size": 134767984, "post_processing_size": null, "dataset_size": 435177731, "size_in_bytes": 569945715}, "fscs": {"description": "The Federal Supreme Court of Switzerland (FSCS) is the last level of appeal in Switzerland and similarly\nto SCOTUS, the court generally hears only the most controversial or otherwise complex cases which have\nnot been sufficiently well solved by lower courts. The court often focus only on small parts of previous\ndecision, where they discuss possible wrong reasoning by the lower court. The Swiss-Judgment-Predict\ndataset (Niklaus et al., 2021) contains more than 85K decisions from the FSCS written in one of three\nlanguages (50K German, 31K French, 4K Italian) from the years 2000 to 2020. The dataset is not parallel,\ni.e., all cases are unique and decisions are written only in a single language. The dataset provides labels\nfor a simplified binary (approval, dismissal) classification task. Given the facts of the case, the goal\nis to predict if the plaintiff's request is valid or partially valid.", "citation": "@InProceedings{niklaus-etal-2021-swiss,\n author = {Niklaus, Joel\n and Chalkidis, Ilias\n and St\u00fcrmer, Matthias},\n title = {Swiss-Court-Predict: A Multilingual Legal Judgment Prediction Benchmark},\n booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop},\n year = {2021},\n location = {Punta Cana, Dominican Republic},\n}\n@inproceedings{chalkidis-etal-2022-fairlex,\n author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and\n Tomada, Letizia and Schwemer, Sebastian Felix and S\u00f8gaard, Anders},\n title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},\n booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},\n year={2022},\n address={Dublin, Ireland}\n}\n", "homepage": "https://github.com/JoelNiklaus/SwissCourtRulingCorpus", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["dismissal", "approval"], "names_file": null, "id": null, "_type": "ClassLabel"}, "decision_language": {"num_classes": 3, "names": ["de", "fr", "it"], "names_file": null, "id": null, "_type": "ClassLabel"}, "legal_area": {"num_classes": 6, "names": ["other", "public law", "penal law", "civil law", "social law", "insurance law"], "names_file": null, "id": null, "_type": "ClassLabel"}, "court_region": {"num_classes": 9, "names": ["n/a", "R\u00e9gion l\u00e9manique", "Z\u00fcrich", "Espace Mittelland", "Northwestern Switzerland", "Eastern Switzerland", "Central Switzerland", "Ticino", "Federation"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fairlex", "config_name": "fscs", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 215061616, "num_examples": 59709, "dataset_name": "fairlex"}, "test": {"name": "test", "num_bytes": 62797755, "num_examples": 17357, "dataset_name": "fairlex"}, "validation": {"name": "validation", "num_bytes": 26578116, "num_examples": 8208, "dataset_name": "fairlex"}}, "download_checksums": {"https://zenodo.org/record/6322643/files/fscs.zip": {"num_bytes": 85400870, "checksum": "c12dba359b9862c4c7d3ddc2b41ab277e11d4a092e5ade9fc5ed85d14b665372"}}, "download_size": 85400870, "post_processing_size": null, "dataset_size": 304437487, "size_in_bytes": 389838357}, "cail": {"description": "The Supreme People's Court of China (CAIL) is the last level of appeal in China and considers cases that\noriginated from the high people's courts concerning matters of national importance. The Chinese AI and Law\nchallenge (CAIL) dataset (Xiao et al., 2018) is a Chinese legal NLP dataset for judgment prediction and\ncontains over 1m criminal cases. The dataset provides labels for relevant article of criminal code\nprediction, charge (type of crime) prediction, imprisonment term (period) prediction, and monetary penalty\nprediction. The updated (soft) version of the CAIL dataset has 104K criminal court cases. The tasks is\ncrime severity prediction task, a multi-class classification task, where given the facts of a case,\nthe goal is to predict how severe was the committed crime with respect to the imprisonment term.\nWe approximate crime severity by the length of imprisonment term, split in 6 clusters\n(0, >=12, >=36, >=60, >=120, >120 months).", "citation": "@article{wang-etal-2021-equality,\n title={Equality before the Law: Legal Judgment Consistency Analysis for Fairness},\n author={Yuzhong Wang and Chaojun Xiao and Shirong Ma and Haoxi Zhong and Cunchao Tu and Tianyang Zhang and Zhiyuan Liu and Maosong Sun},\n year={2021},\n journal={Science China - Information Sciences},\n url={https://arxiv.org/abs/2103.13868}\n}\n@inproceedings{chalkidis-etal-2022-fairlex,\n author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and\n Tomada, Letizia and Schwemer, Sebastian Felix and S\u00f8gaard, Anders},\n title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},\n booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},\n year={2022},\n address={Dublin, Ireland}\n}\n", "homepage": "https://github.com/thunlp/LegalPLMs", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 6, "names": ["0", "<=12", "<=36", "<=60", "<=120", ">120"], "names_file": null, "id": null, "_type": "ClassLabel"}, "defendant_gender": {"num_classes": 2, "names": ["male", "female"], "names_file": null, "id": null, "_type": "ClassLabel"}, "court_region": {"num_classes": 7, "names": ["Beijing", "Liaoning", "Hunan", "Guangdong", "Sichuan", "Guangxi", "Zhejiang"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fairlex", "config_name": "cail", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 257477656, "num_examples": 80000, "dataset_name": "fairlex"}, "test": {"name": "test", "num_bytes": 31819177, "num_examples": 12000, "dataset_name": "fairlex"}, "validation": {"name": "validation", "num_bytes": 37149169, "num_examples": 12000, "dataset_name": "fairlex"}}, "download_checksums": {"https://zenodo.org/record/6322643/files/cail.zip": {"num_bytes": 112994247, "checksum": "e0d377976056300b728ca920cb38465bece2fcbbf7a377a1283defc5c025e23c"}}, "download_size": 112994247, "post_processing_size": null, "dataset_size": 326446002, "size_in_bytes": 439440249}} |
