Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
extended
ArXiv:
Tags:
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Delete legacy dataset_infos.json

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- "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.",
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- "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}",
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- "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.",
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- "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}",
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