# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.""" import csv import json import textwrap import datasets MAIN_CITATION = """\ @article{chalkidis-etal-2021-lexglue, title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English}, author={Chalkidis, Ilias and Jana, Abhik and Hartung, Dirk and Bommarito, Michael and Androutsopoulos, Ion and Katz, Daniel Martin and Aletras, Nikolaos}, year={2021}, eprint={2110.00976}, archivePrefix={arXiv}, primaryClass={cs.CL}, note = {arXiv: 2110.00976}, }""" _DESCRIPTION = """\ Legal General Language Understanding Evaluation (LexGLUE) benchmark is a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks """ ECTHR_ARTICLES = ["2", "3", "5", "6", "8", "9", "10", "11", "14", "P1-1"] EUROVOC_CONCEPTS = [ "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", ] LEDGAR_CATEGORIES = [ "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", ] SCDB_ISSUE_AREAS = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"] UNFAIR_CATEGORIES = [ "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration", ] CASEHOLD_LABELS = ["0", "1", "2", "3", "4"] class LexGlueConfig(datasets.BuilderConfig): """BuilderConfig for LexGLUE.""" def __init__( self, text_column, label_column, url, data_url, data_file, citation, label_classes=None, multi_label=None, dev_column="dev", **kwargs, ): """BuilderConfig for LexGLUE. Args: text_column: ``string`, name of the column in the jsonl file corresponding to the text label_column: `string`, name of the column in the jsonl file corresponding to the label url: `string`, url for the original project data_url: `string`, url to download the zip file from data_file: `string`, filename for data set citation: `string`, citation for the data set url: `string`, url for information about the data set label_classes: `list[string]`, the list of classes if the label is categorical. If not provided, then the label will be of type `datasets.Value('float32')`. multi_label: `boolean`, True if the task is multi-label dev_column: `string`, name for the development subset **kwargs: keyword arguments forwarded to super. """ super(LexGlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) self.text_column = text_column self.label_column = label_column self.label_classes = label_classes self.multi_label = multi_label self.dev_column = dev_column self.url = url self.data_url = data_url self.data_file = data_file self.citation = citation class LexGLUE(datasets.GeneratorBasedBuilder): """LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. Version 1.0""" BUILDER_CONFIGS = [ LexGlueConfig( name="ecthr_a", description=textwrap.dedent( """\ The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of the ECHR that were violated (if any).""" ), text_column="facts", label_column="violated_articles", label_classes=ECTHR_ARTICLES, multi_label=True, dev_column="dev", data_url="https://zenodo.org/record/5532997/files/ecthr.tar.gz", data_file="ecthr.jsonl", url="https://archive.org/details/ECtHR-NAACL2021", citation=textwrap.dedent( """\ @inproceedings{chalkidis-etal-2021-paragraph, title = "Paragraph-level Rationale Extraction through Regularization: A case study on {E}uropean Court of Human Rights Cases", author = "Chalkidis, Ilias and Fergadiotis, Manos and Tsarapatsanis, Dimitrios and Aletras, Nikolaos and Androutsopoulos, Ion and Malakasiotis, Prodromos", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.22", doi = "10.18653/v1/2021.naacl-main.22", pages = "226--241", } }""" ), ), LexGlueConfig( name="ecthr_b", description=textwrap.dedent( """\ The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of ECHR that were allegedly violated (considered by the court).""" ), text_column="facts", label_column="allegedly_violated_articles", label_classes=ECTHR_ARTICLES, multi_label=True, dev_column="dev", url="https://archive.org/details/ECtHR-NAACL2021", data_url="https://zenodo.org/record/5532997/files/ecthr.tar.gz", data_file="ecthr.jsonl", citation=textwrap.dedent( """\ @inproceedings{chalkidis-etal-2021-paragraph, title = "Paragraph-level Rationale Extraction through Regularization: A case study on {E}uropean Court of Human Rights Cases", author = "Chalkidis, Ilias and Fergadiotis, Manos and Tsarapatsanis, Dimitrios and Aletras, Nikolaos and Androutsopoulos, Ion and Malakasiotis, Prodromos", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", year = "2021", address = "Online", url = "https://aclanthology.org/2021.naacl-main.22", } }""" ), ), LexGlueConfig( name="eurlex", description=textwrap.dedent( """\ European Union (EU) legislation is published in EUR-Lex portal. All EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, a multilingual thesaurus maintained by the Publications Office. The current version of EuroVoc contains more than 7k concepts referring to various activities of the EU and its Member States (e.g., economics, health-care, trade). Given a document, the task is to predict its EuroVoc labels (concepts).""" ), text_column="text", label_column="labels", label_classes=EUROVOC_CONCEPTS, multi_label=True, dev_column="dev", url="https://zenodo.org/record/5363165#.YVJOAi8RqaA", data_url="https://zenodo.org/record/5532997/files/eurlex.tar.gz", data_file="eurlex.jsonl", citation=textwrap.dedent( """\ @inproceedings{chalkidis-etal-2021-multieurlex, author = {Chalkidis, Ilias and Fergadiotis, Manos and Androutsopoulos, Ion}, title = {MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer}, booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, year = {2021}, location = {Punta Cana, Dominican Republic}, } }""" ), ), LexGlueConfig( name="scotus", description=textwrap.dedent( """\ The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. This is a single-label multi-class classification task, where given a document (court opinion), the task is to predict the relevant issue areas. The 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute).""" ), text_column="text", label_column="issueArea", label_classes=SCDB_ISSUE_AREAS, multi_label=False, dev_column="dev", url="http://scdb.wustl.edu/data.php", data_url="https://zenodo.org/record/5532997/files/scotus.tar.gz", data_file="scotus.jsonl", citation=textwrap.dedent( """\ @misc{spaeth2020, author = {Harold J. Spaeth and Lee Epstein and Andrew D. Martin, Jeffrey A. Segal and Theodore J. Ruger and Sara C. Benesh}, year = {2020}, title ={{Supreme Court Database, Version 2020 Release 01}}, url= {http://Supremecourtdatabase.org}, howpublished={Washington University Law} }""" ), ), LexGlueConfig( name="ledgar", description=textwrap.dedent( """\ LEDGAR dataset aims contract provision (paragraph) classification. The contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) filings, which are publicly available from EDGAR. Each label represents the single main topic (theme) of the corresponding contract provision.""" ), text_column="text", label_column="clause_type", label_classes=LEDGAR_CATEGORIES, multi_label=False, dev_column="dev", url="https://metatext.io/datasets/ledgar", data_url="https://zenodo.org/record/5532997/files/ledgar.tar.gz", data_file="ledgar.jsonl", citation=textwrap.dedent( """\ @inproceedings{tuggener-etal-2020-ledgar, title = "{LEDGAR}: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts", author = {Tuggener, Don and von D{\"a}niken, Pius and Peetz, Thomas and Cieliebak, Mark}, booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", year = "2020", address = "Marseille, France", url = "https://aclanthology.org/2020.lrec-1.155", } }""" ), ), LexGlueConfig( name="unfair_tos", description=textwrap.dedent( """\ The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of unfair contractual terms (sentences), meaning terms that potentially violate user rights according to the European consumer law.""" ), text_column="text", label_column="labels", label_classes=UNFAIR_CATEGORIES, multi_label=True, dev_column="val", url="http://claudette.eui.eu", data_url="https://zenodo.org/record/5532997/files/unfair_tos.tar.gz", data_file="unfair_tos.jsonl", citation=textwrap.dedent( """\ @article{lippi-etal-2019-claudette, title = "{CLAUDETTE}: an automated detector of potentially unfair clauses in online terms of service", author = {Lippi, Marco and Pałka, Przemysław and Contissa, Giuseppe and Lagioia, Francesca and Micklitz, Hans-Wolfgang and Sartor, Giovanni and Torroni, Paolo}, journal = "Artificial Intelligence and Law", year = "2019", publisher = "Springer", url = "https://doi.org/10.1007/s10506-019-09243-2", pages = "117--139", }""" ), ), LexGlueConfig( name="case_hold", description=textwrap.dedent( """\ The CaseHOLD (Case Holdings on Legal Decisions) dataset contains approx. 53k multiple choice questions about holdings of US court cases from the Harvard Law Library case law corpus. Holdings are short summaries of legal rulings accompany referenced decisions relevant for the present case. The input consists of an excerpt (or prompt) from a court decision, containing a reference to a particular case, while the holding statement is masked out. The model must identify the correct (masked) holding statement from a selection of five choices.""" ), text_column="text", label_column="labels", dev_column="dev", multi_label=False, label_classes=CASEHOLD_LABELS, url="https://github.com/reglab/casehold", data_url="https://zenodo.org/record/5532997/files/casehold.tar.gz", data_file="casehold.csv", citation=textwrap.dedent( """\ @inproceedings{Zheng2021, author = {Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho}, title = {When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset}, year = {2021}, booktitle = {International Conference on Artificial Intelligence and Law}, }""" ), ), ] def _info(self): if self.config.name == "case_hold": features = { "context": datasets.Value("string"), "endings": datasets.features.Sequence(datasets.Value("string")), } elif "ecthr" in self.config.name: features = {"text": datasets.features.Sequence(datasets.Value("string"))} else: features = {"text": datasets.Value("string")} if self.config.multi_label: features["labels"] = datasets.features.Sequence(datasets.ClassLabel(names=self.config.label_classes)) else: features["label"] = datasets.ClassLabel(names=self.config.label_classes) return datasets.DatasetInfo( description=self.config.description, features=datasets.Features(features), homepage=self.config.url, citation=self.config.citation + "\n" + MAIN_CITATION, ) def _split_generators(self, dl_manager): 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": 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": 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": self.config.data_file, "split": self.config.dev_column, "files": dl_manager.iter_archive(archive), }, ), ] 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)} 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_, { "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: 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