--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K DatasetSourceSub-domainTask TypeClasses ECtHR (Task A) Chalkidis et al. (2019) ECHRMulti-label classification10+1 ECtHR (Task B) Chalkidis et al. (2021a) ECHRMulti-label classification 10+1 SCOTUS Spaeth et al. (2020)US LawMulti-class classification14 EUR-LEX Chalkidis et al. (2021b)EU LawMulti-label classification100 LEDGAR Tuggener et al. (2020)ContractsMulti-class classification100 UNFAIR-ToS Lippi et al. (2019)ContractsMulti-label classification8+1 CaseHOLDZheng et al. (2021)US LawMultiple choice QAn/a #### ecthr_a 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). #### ecthr_b 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). #### scotus 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). #### eurlex 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). #### ledgar 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. #### unfair_tos 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. #### case_hold The CaseHOLD (Case Holdings on Legal Decisions) dataset includes 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. The current leaderboard includes several Transformer-based (Vaswaniet al., 2017) pre-trained language models, which achieve state-of-the-art performance in most NLP tasks (Bommasani et al., 2021) and NLU benchmarks (Wang et al., 2019a). Results reported by [Chalkidis et al. (2021)](https://arxiv.org/abs/2110.00976): *Task-wise Test Results*
DatasetECtHR AECtHR BSCOTUSEUR-LEXLEDGARUNFAIR-ToSCaseHOLD
Modelμ-F1 / m-F1 μ-F1 / m-F1 μ-F1 / m-F1 μ-F1 / m-F1 μ-F1 / m-F1 μ-F1 / m-F1μ-F1 / m-F1
TFIDF+SVM 64.7 / 51.7 74.6 / 65.1 78.2 / 69.5 71.3 / 51.4 87.2 / 82.4 95.4 / 78.8n/a
Medium-sized Models (L=12, H=768, A=12)
BERT 71.2 / 63.6 79.7 / 73.4 68.3 / 58.3 71.4 / 57.2 87.6 / 81.8 95.6 / 81.3 70.8
RoBERTa 69.2 / 59.0 77.3 / 68.9 71.6 / 62.0 71.9 / 57.9 87.9 / 82.3 95.2 / 79.2 71.4
DeBERTa 70.0 / 60.8 78.8 / 71.0 71.1 / 62.7 72.1 / 57.4 88.2 / 83.1 95.5 / 80.3 72.6
Longformer 69.9 / 64.7 79.4 / 71.7 72.9 / 64.0 71.6 / 57.7 88.2 / 83.0 95.5 / 80.9 71.9
BigBird 70.0 / 62.9 78.8 / 70.9 72.8 / 62.0 71.5 / 56.8 87.8 / 82.6 95.7 / 81.3 70.8
Legal-BERT 70.0 / 64.0 80.4 / 74.7 76.4 / 66.5 72.1 / 57.4 88.2 / 83.0 96.0 / 83.0 75.3
CaseLaw-BERT 69.8 / 62.9 78.8 / 70.3 76.6 / 65.9 70.7 / 56.6 88.3 / 83.0 96.0 / 82.3 75.4
Large-sized Models (L=24, H=1024, A=18)
RoBERTa 73.8 / 67.6 79.8 / 71.6 75.5 / 66.3 67.9 / 50.3 88.6 / 83.6 95.8 / 81.6 74.4
*Averaged (Mean over Tasks) Test Results*
AveragingArithmeticHarmonicGeometric
Modelμ-F1 / m-F1 μ-F1 / m-F1 μ-F1 / m-F1
Medium-sized Models (L=12, H=768, A=12)
BERT 77.8 / 69.5 76.7 / 68.2 77.2 / 68.8
RoBERTa 77.8 / 68.7 76.8 / 67.5 77.3 / 68.1
DeBERTa 78.3 / 69.7 77.4 / 68.5 77.8 / 69.1
Longformer 78.5 / 70.5 77.5 / 69.5 78.0 / 70.0
BigBird 78.2 / 69.6 77.2 / 68.5 77.7 / 69.0
Legal-BERT 79.8 / 72.0 78.9 / 70.8 79.3 / 71.4
CaseLaw-BERT 79.4 / 70.9 78.5 / 69.7 78.9 / 70.3
Large-sized Models (L=24, H=1024, A=18)
RoBERTa 79.4 / 70.8 78.4 / 69.1 78.9 / 70.0
### Languages We only consider English datasets, to make experimentation easier for researchers across the globe. ## Dataset Structure ### Data Instances #### ecthr_a An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "labels": [6] } ``` #### ecthr_b An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "label": [5, 6] } ``` #### scotus An example of 'train' looks as follows. ```json { "text": "Per Curiam\nSUPREME COURT OF THE UNITED STATES\nRANDY WHITE, WARDEN v. ROGER L. WHEELER\n Decided December 14, 2015\nPER CURIAM.\nA death sentence imposed by a Kentucky trial court and\naffirmed by the ...", "label": 8 } ``` #### eurlex An example of 'train' looks as follows. ```json { "text": "COMMISSION REGULATION (EC) No 1629/96 of 13 August 1996 on an invitation to tender for the refund on export of wholly milled round grain rice to certain third countries ...", "labels": [4, 20, 21, 35, 68] } ``` #### ledgar An example of 'train' looks as follows. ```json { "text": "All Taxes shall be the financial responsibility of the party obligated to pay such Taxes as determined by applicable law and neither party is or shall be liable at any time for any of the other party ...", "label": 32 } ``` #### unfair_tos An example of 'train' looks as follows. ```json { "text": "tinder may terminate your account at any time without notice if it believes that you have violated this agreement.", "label": 2 } ``` #### casehold An example of 'test' looks as follows. ```json { "context": "In Granato v. City and County of Denver, No. CIV 11-0304 MSK/BNB, 2011 WL 3820730 (D.Colo. Aug. 20, 2011), the Honorable Marcia S. Krieger, now-Chief United States District Judge for the District of Colorado, ruled similarly: At a minimum, a party asserting a Mo-nell claim must plead sufficient facts to identify ... to act pursuant to City or State policy, custom, decision, ordinance, re d 503, 506-07 (3d Cir.l985)().", "endings": ["holding that courts are to accept allegations in the complaint as being true including monell policies and writing that a federal court reviewing the sufficiency of a complaint has a limited task", "holding that for purposes of a class certification motion the court must accept as true all factual allegations in the complaint and may draw reasonable inferences therefrom", "recognizing that the allegations of the complaint must be accepted as true on a threshold motion to dismiss", "holding that a court need not accept as true conclusory allegations which are contradicted by documents referred to in the complaint", "holding that where the defendant was in default the district court correctly accepted the fact allegations of the complaint as true" ], "label": 0 } ``` ### Data Fields #### ecthr_a - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description). - `labels`: a list of classification labels (a list of violated ECHR articles, if any) .
List of ECHR articles "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1"
#### ecthr_b - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description) - `labels`: a list of classification labels (a list of articles considered).
List of ECHR articles "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1"
#### scotus - `text`: a `string` feature (the court opinion). - `label`: a classification label (the relevant issue area).
List of issue areas (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action)
#### eurlex - `text`: a `string` feature (an EU law). - `labels`: a list of classification labels (a list of relevant EUROVOC concepts).
List of EUROVOC concepts The list is very long including 100 EUROVOC concepts. You can find the EUROVOC concepts descriptors here.
#### ledgar - `text`: a `string` feature (a contract provision/paragraph). - `label`: a classification label (the type of contract provision).
List of contract provision types "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",
#### unfair_tos - `text`: a `string` feature (a ToS sentence) - `labels`: a list of classification labels (a list of unfair types, if any).
List of unfair types "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration"
#### casehold - `context`: a `string` feature (a context sentence incl. a masked holding statement). - `holdings`: a list of `string` features (a list of candidate holding statements). - `label`: a classification label (the id of the original/correct holding). ### Data Splits
Dataset TrainingDevelopmentTestTotal
ECtHR (Task A)9,0001,0001,00011,000
ECtHR (Task B)9,0001,0001,00011,000
SCOTUS5,0001,4001,4007,800
EUR-LEX55,0005,0005,00065,000
LEDGAR60,00010,00010,00080,000
UNFAIR-ToS5,5322,2751,6079,414
CaseHOLD45,0003,9003,90052,800
## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data
DatasetSourceSub-domainTask Type
ECtHR (Task A) Chalkidis et al. (2019) ECHRMulti-label classification
ECtHR (Task B) Chalkidis et al. (2021a) ECHRMulti-label classification
SCOTUS Spaeth et al. (2020)US LawMulti-class classification
EUR-LEX Chalkidis et al. (2021b)EU LawMulti-label classification
LEDGAR Tuggener et al. (2020)ContractsMulti-class classification
UNFAIR-ToS Lippi et al. (2019)ContractsMulti-label classification
CaseHOLDZheng et al. (2021)US LawMultiple choice QA
#### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Curators *Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.* ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information [*Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.*](https://arxiv.org/abs/2110.00976) ``` @inproceedings{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={2022}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, address={Dubln, Ireland}, } ``` ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.