Datasets:

coastalcph
/

lex_glue

@@ -1,792 +0,0 @@
----
-pretty_name: LexGLUE
-annotations_creators:
-- found
-language_creators:
-- found
-language:
-- en
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- extended
-task_categories:
-- question-answering
-- text-classification
-task_ids:
-- multi-class-classification
-- multi-label-classification
-- multiple-choice-qa
-- topic-classification
-configs:
-- case_hold
-- ecthr_a
-- ecthr_b
-- eurlex
-- ledgar
-- scotus
-- unfair_tos
-dataset_info:
-- config_name: ecthr_a
-  features:
-  - name: text
-    sequence: string
-  - name: labels
-    sequence:
-      class_label:
-        names:
-          0: '2'
-          1: '3'
-          2: '5'
-          3: '6'
-          4: '8'
-          5: '9'
-          6: '10'
-          7: '11'
-          8: '14'
-          9: P1-1
-  splits:
-  - name: train
-    num_bytes: 89637461
-    num_examples: 9000
-  - name: test
-    num_bytes: 11884180
-    num_examples: 1000
-  - name: validation
-    num_bytes: 10985180
-    num_examples: 1000
-  download_size: 32852475
-  dataset_size: 112506821
-- config_name: ecthr_b
-  features:
-  - name: text
-    sequence: string
-  - name: labels
-    sequence:
-      class_label:
-        names:
-          0: '2'
-          1: '3'
-          2: '5'
-          3: '6'
-          4: '8'
-          5: '9'
-          6: '10'
-          7: '11'
-          8: '14'
-          9: P1-1
-  splits:
-  - name: train
-    num_bytes: 89657661
-    num_examples: 9000
-  - name: test
-    num_bytes: 11886940
-    num_examples: 1000
-  - name: validation
-    num_bytes: 10987828
-    num_examples: 1000
-  download_size: 32852475
-  dataset_size: 112532429
-- config_name: eurlex
-  features:
-  - name: text
-    dtype: string
-  - name: labels
-    sequence:
-      class_label:
-        names:
-          0: '100163'
-          1: '100168'
-          2: '100169'
-          3: '100170'
-          4: '100171'
-          5: '100172'
-          6: '100173'
-          7: '100174'
-          8: '100175'
-          9: '100176'
-          10: '100177'
-          11: '100179'
-          12: '100180'
-          13: '100183'
-          14: '100184'
-          15: '100185'
-          16: '100186'
-          17: '100187'
-          18: '100189'
-          19: '100190'
-          20: '100191'
-          21: '100192'
-          22: '100193'
-          23: '100194'
-          24: '100195'
-          25: '100196'
-          26: '100197'
-          27: '100198'
-          28: '100199'
-          29: '100200'
-          30: '100201'
-          31: '100202'
-          32: '100204'
-          33: '100205'
-          34: '100206'
-          35: '100207'
-          36: '100212'
-          37: '100214'
-          38: '100215'
-          39: '100220'
-          40: '100221'
-          41: '100222'
-          42: '100223'
-          43: '100224'
-          44: '100226'
-          45: '100227'
-          46: '100229'
-          47: '100230'
-          48: '100231'
-          49: '100232'
-          50: '100233'
-          51: '100234'
-          52: '100235'
-          53: '100237'
-          54: '100238'
-          55: '100239'
-          56: '100240'
-          57: '100241'
-          58: '100242'
-          59: '100243'
-          60: '100244'
-          61: '100245'
-          62: '100246'
-          63: '100247'
-          64: '100248'
-          65: '100249'
-          66: '100250'
-          67: '100252'
-          68: '100253'
-          69: '100254'
-          70: '100255'
-          71: '100256'
-          72: '100257'
-          73: '100258'
-          74: '100259'
-          75: '100260'
-          76: '100261'
-          77: '100262'
-          78: '100263'
-          79: '100264'
-          80: '100265'
-          81: '100266'
-          82: '100268'
-          83: '100269'
-          84: '100270'
-          85: '100271'
-          86: '100272'
-          87: '100273'
-          88: '100274'
-          89: '100275'
-          90: '100276'
-          91: '100277'
-          92: '100278'
-          93: '100279'
-          94: '100280'
-          95: '100281'
-          96: '100282'
-          97: '100283'
-          98: '100284'
-          99: '100285'
-  splits:
-  - name: train
-    num_bytes: 390770289
-    num_examples: 55000
-  - name: test
-    num_bytes: 59739102
-    num_examples: 5000
-  - name: validation
-    num_bytes: 41544484
-    num_examples: 5000
-  download_size: 125413277
-  dataset_size: 492053875
-- config_name: scotus
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: '1'
-          1: '2'
-          2: '3'
-          3: '4'
-          4: '5'
-          5: '6'
-          6: '7'
-          7: '8'
-          8: '9'
-          9: '10'
-          10: '11'
-          11: '12'
-          12: '13'
-  splits:
-  - name: train
-    num_bytes: 178959320
-    num_examples: 5000
-  - name: test
-    num_bytes: 76213283
-    num_examples: 1400
-  - name: validation
-    num_bytes: 75600247
-    num_examples: 1400
-  download_size: 104763335
-  dataset_size: 330772850
-- config_name: ledgar
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: Adjustments
-          1: Agreements
-          2: Amendments
-          3: Anti-Corruption Laws
-          4: Applicable Laws
-          5: Approvals
-          6: Arbitration
-          7: Assignments
-          8: Assigns
-          9: Authority
-          10: Authorizations
-          11: Base Salary
-          12: Benefits
-          13: Binding Effects
-          14: Books
-          15: Brokers
-          16: Capitalization
-          17: Change In Control
-          18: Closings
-          19: Compliance With Laws
-          20: Confidentiality
-          21: Consent To Jurisdiction
-          22: Consents
-          23: Construction
-          24: Cooperation
-          25: Costs
-          26: Counterparts
-          27: Death
-          28: Defined Terms
-          29: Definitions
-          30: Disability
-          31: Disclosures
-          32: Duties
-          33: Effective Dates
-          34: Effectiveness
-          35: Employment
-          36: Enforceability
-          37: Enforcements
-          38: Entire Agreements
-          39: Erisa
-          40: Existence
-          41: Expenses
-          42: Fees
-          43: Financial Statements
-          44: Forfeitures
-          45: Further Assurances
-          46: General
-          47: Governing Laws
-          48: Headings
-          49: Indemnifications
-          50: Indemnity
-          51: Insurances
-          52: Integration
-          53: Intellectual Property
-          54: Interests
-          55: Interpretations
-          56: Jurisdictions
-          57: Liens
-          58: Litigations
-          59: Miscellaneous
-          60: Modifications
-          61: No Conflicts
-          62: No Defaults
-          63: No Waivers
-          64: Non-Disparagement
-          65: Notices
-          66: Organizations
-          67: Participations
-          68: Payments
-          69: Positions
-          70: Powers
-          71: Publicity
-          72: Qualifications
-          73: Records
-          74: Releases
-          75: Remedies
-          76: Representations
-          77: Sales
-          78: Sanctions
-          79: Severability
-          80: Solvency
-          81: Specific Performance
-          82: Submission To Jurisdiction
-          83: Subsidiaries
-          84: Successors
-          85: Survival
-          86: Tax Withholdings
-          87: Taxes
-          88: Terminations
-          89: Terms
-          90: Titles
-          91: Transactions With Affiliates
-          92: Use Of Proceeds
-          93: Vacations
-          94: Venues
-          95: Vesting
-          96: Waiver Of Jury Trials
-          97: Waivers
-          98: Warranties
-          99: Withholdings
-  splits:
-  - name: train
-    num_bytes: 43358315
-    num_examples: 60000
-  - name: test
-    num_bytes: 6845585
-    num_examples: 10000
-  - name: validation
-    num_bytes: 7143592
-    num_examples: 10000
-  download_size: 16255623
-  dataset_size: 57347492
-- config_name: unfair_tos
-  features:
-  - name: text
-    dtype: string
-  - name: labels
-    sequence:
-      class_label:
-        names:
-          0: Limitation of liability
-          1: Unilateral termination
-          2: Unilateral change
-          3: Content removal
-          4: Contract by using
-          5: Choice of law
-          6: Jurisdiction
-          7: Arbitration
-  splits:
-  - name: train
-    num_bytes: 1041790
-    num_examples: 5532
-  - name: test
-    num_bytes: 303107
-    num_examples: 1607
-  - name: validation
-    num_bytes: 452119
-    num_examples: 2275
-  download_size: 511342
-  dataset_size: 1797016
-- config_name: case_hold
-  features:
-  - name: context
-    dtype: string
-  - name: endings
-    sequence: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: '0'
-          1: '1'
-          2: '2'
-          3: '3'
-          4: '4'
-  splits:
-  - name: train
-    num_bytes: 74781766
-    num_examples: 45000
-  - name: test
-    num_bytes: 5989964
-    num_examples: 3600
-  - name: validation
-    num_bytes: 6474615
-    num_examples: 3900
-  download_size: 30422703
-  dataset_size: 87246345
----
-# Dataset Card for "LexGLUE"
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-## Dataset Description
-- **Homepage:** https://github.com/coastalcph/lex-glue
-- **Repository:** https://github.com/coastalcph/lex-glue
-- **Paper:** https://arxiv.org/abs/2110.00976
-- **Leaderboard:** https://github.com/coastalcph/lex-glue
-- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk)
-### Dataset Summary
-Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the *Legal General Language Understanding Evaluation (LexGLUE) benchmark*, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE.
-As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks.
-LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: https://github.com/coastalcph/lex-glue.
-### Supported Tasks and Leaderboards
-The supported tasks are the following:
-<table>
-<tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><td>Classes</td><tr>
-<tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td><td>10+1</td></tr>
-<tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td><td>10+1</td></tr>
-<tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td><td>14</td></tr>
-<tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td><td>100</td></tr>
-<tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td><td>100</td></tr>
-<tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td><td>8+1</td></tr>
-<tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td><td>n/a</td></tr>
-</table>
-#### 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*
-<table>
-        <tr><td><b>Dataset</b></td><td><b>ECtHR A</b></td><td><b>ECtHR B</b></td><td><b>SCOTUS</b></td><td><b>EUR-LEX</b></td><td><b>LEDGAR</b></td><td><b>UNFAIR-ToS</b></td><td><b>CaseHOLD</b></td></tr>
-<tr><td><b>Model</b></td><td>μ-F1  / m-F1 </td><td>μ-F1  / m-F1 </td><td>μ-F1  / m-F1 </td><td>μ-F1  / m-F1 </td><td>μ-F1  / m-F1 </td><td>μ-F1  / m-F1</td><td>μ-F1 / m-F1  </td></tr>
-<tr><td>TFIDF+SVM</td><td> 64.7 / 51.7  </td><td>74.6 / 65.1 </td><td> <b>78.2</b> / <b>69.5</b> </td><td>71.3  / 51.4 </td><td>87.2   / 82.4 </td><td>95.4  / 78.8</td><td>n/a   </td></tr>
-<tr><td colspan="8" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr>
-<td>BERT</td> <td> 71.2 /  63.6 </td> <td> 79.7 /  73.4 </td> <td> 68.3 /  58.3 </td> <td> 71.4 /  57.2 </td> <td> 87.6 /  81.8 </td> <td> 95.6 /  81.3 </td> <td> 70.8 </td> </tr>
-<td>RoBERTa</td> <td> 69.2 /  59.0 </td> <td> 77.3 /  68.9 </td> <td> 71.6 /  62.0 </td> <td> 71.9 /  <b>57.9</b> </td> <td> 87.9 /  82.3 </td> <td> 95.2 /  79.2 </td> <td> 71.4 </td> </tr>
-<td>DeBERTa</td> <td> 70.0 /  60.8 </td> <td> 78.8 /  71.0 </td> <td> 71.1 /  62.7 </td> <td> <b>72.1</b> /  57.4 </td> <td> 88.2 /  83.1 </td> <td> 95.5 /  80.3 </td> <td> 72.6 </td> </tr>
-<td>Longformer</td> <td> 69.9 / 64.7 </td> <td> 79.4 /  71.7 </td> <td> 72.9 /  64.0 </td> <td> 71.6 /  57.7 </td> <td> 88.2 /  83.0 </td> <td> 95.5 /  80.9 </td> <td> 71.9 </td> </tr>
-<td>BigBird</td> <td> 70.0 /  62.9 </td> <td> 78.8 /  70.9 </td> <td> 72.8 /  62.0 </td> <td> 71.5 /  56.8 </td> <td> 87.8 /  82.6 </td> <td> 95.7 /  81.3 </td> <td> 70.8 </td> </tr>
-<td>Legal-BERT</td> <td> 70.0 /  64.0 </td> <td> <b>80.4</b> /  <b>74.7</b> </td> <td> 76.4 /  66.5 </td> <td> <b>72.1</b> /  57.4 </td> <td> 88.2 /  83.0 </td> <td> <b>96.0</b> /  <b>83.0</b> </td> <td> 75.3 </td> </tr>
-<td>CaseLaw-BERT</td> <td> 69.8 /  62.9 </td> <td> 78.8 /  70.3 </td> <td> 76.6 /  65.9 </td> <td> 70.7 /  56.6 </td> <td> 88.3 /  83.0 </td> <td> <b>96.0</b> /  82.3 </td> <td> <b>75.4</b> </td> </tr>
-<tr><td colspan="8" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr>
-<tr><td>RoBERTa</td> <td> <b>73.8</b> /  <b>67.6</b> </td> <td> 79.8 /  71.6 </td> <td> 75.5 /  66.3 </td> <td> 67.9 /  50.3 </td> <td> <b>88.6</b> /  <b>83.6</b> </td> <td> 95.8 /  81.6 </td> <td> 74.4 </td> </tr>
-</table>
-*Averaged (Mean over Tasks) Test Results*
-<table>
-<tr><td><b>Averaging</b></td><td><b>Arithmetic</b></td><td><b>Harmonic</b></td><td><b>Geometric</b></td></tr>
-<tr><td><b>Model</b></td><td>μ-F1  / m-F1 </td><td>μ-F1  / m-F1 </td><td>μ-F1  / m-F1 </td></tr>
-<tr><td colspan="4" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr>
-<tr><td>BERT</td><td> 77.8 /  69.5 </td><td> 76.7 /  68.2 </td><td> 77.2 /  68.8 </td></tr>
-<tr><td>RoBERTa</td><td> 77.8 /  68.7 </td><td> 76.8 /  67.5 </td><td> 77.3 /  68.1 </td></tr>
-<tr><td>DeBERTa</td><td> 78.3 /  69.7 </td><td> 77.4 /  68.5 </td><td> 77.8 /  69.1 </td></tr>
-<tr><td>Longformer</td><td> 78.5 /  70.5 </td><td> 77.5 /  69.5 </td><td> 78.0 /  70.0 </td></tr>
-<tr><td>BigBird</td><td> 78.2 /  69.6 </td><td> 77.2 /  68.5 </td><td> 77.7 /  69.0 </td></tr>
-<tr><td>Legal-BERT</td><td> <b>79.8</b> /  <b>72.0</b> </td><td> <b>78.9</b> /  <b>70.8</b> </td><td> <b>79.3</b> /  <b>71.4</b> </td></tr>
-<tr><td>CaseLaw-BERT</td><td> 79.4 /  70.9 </td><td> 78.5 /  69.7 </td><td> 78.9 /  70.3 </td></tr>
-<tr><td colspan="4" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr>
-<tr><td>RoBERTa</td><td> 79.4 /  70.8 </td><td> 78.4 /  69.1 </td><td> 78.9 /  70.0 </td></tr>
-</table>
-### 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)(<HOLDING>).",
-  "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) .
-<details>
-  <summary>List of ECHR articles</summary>
- "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1"
-</details>
-#### 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).
-<details>
-  <summary>List of ECHR articles</summary>
- "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1"
-</details>
-#### scotus
-- `text`: a `string` feature (the court opinion).
-- `label`: a classification label (the relevant issue area).
-<details>
-  <summary>List of issue areas</summary>
-(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)
-</details>
-#### eurlex
-- `text`: a `string` feature (an EU law).
-- `labels`: a list of classification labels (a list of relevant EUROVOC concepts).
-<details>
-  <summary>List of EUROVOC concepts</summary>
-  The list is very long including 100 EUROVOC concepts. You can find the EUROVOC concepts descriptors <a href="https://raw.githubusercontent.com/nlpaueb/multi-eurlex/master/data/eurovoc_descriptors.json">here</a>.
-</details>
-#### ledgar
-- `text`: a `string` feature (a contract provision/paragraph).
-- `label`: a classification label (the type of contract provision).
-<details>
-  <summary>List of contract provision types</summary>
-"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",
-</details>
-#### unfair_tos
-- `text`: a `string` feature (a ToS sentence)
-- `labels`: a list of classification labels (a list of unfair types, if any).
-<details>
-  <summary>List of unfair types</summary>
-    "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration"
-</details>
-#### 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
-<table>
-<tr><td>Dataset </td><td>Training</td><td>Development</td><td>Test</td><td>Total</td></tr>
-<tr><td>ECtHR (Task A)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr>
-<tr><td>ECtHR (Task B)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr>
-<tr><td>SCOTUS</td><td>5,000</td><td>1,400</td><td>1,400</td><td>7,800</td></tr>
-<tr><td>EUR-LEX</td><td>55,000</td><td>5,000</td><td>5,000</td><td>65,000</td></tr>
-<tr><td>LEDGAR</td><td>60,000</td><td>10,000</td><td>10,000</td><td>80,000</td></tr>
-<tr><td>UNFAIR-ToS</td><td>5,532</td><td>2,275</td><td>1,607</td><td>9,414</td></tr>
-<tr><td>CaseHOLD</td><td>45,000</td><td>3,900</td><td>3,900</td><td>52,800</td></tr>
-</table>
-## 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
-<table>
-<tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><tr>
-<tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td></tr>
-<tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td></tr>
-<tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td></tr>
-<tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td></tr>
-<tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td></tr>
-<tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td></tr>
-<tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td></tr>
-</table>
-#### 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.

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- {"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"], "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@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"], "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": 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"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": 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617467152}, "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"], "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"], "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"], "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"], "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}}

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@@ -1,659 +0,0 @@
-# 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",
-    "100168",
-    "100169",
-    "100170",
-    "100171",
-    "100172",
-    "100173",
-    "100174",
-    "100175",
-    "100176",
-    "100177",
-    "100179",
-    "100180",
-    "100183",
-    "100184",
-    "100185",
-    "100186",
-    "100187",
-    "100189",
-    "100190",
-    "100191",
-    "100192",
-    "100193",
-    "100194",
-    "100195",
-    "100196",
-    "100197",
-    "100198",
-    "100199",
-    "100200",
-    "100201",
-    "100202",
-    "100204",
-    "100205",
-    "100206",
-    "100207",
-    "100212",
-    "100214",
-    "100215",
-    "100220",
-    "100221",
-    "100222",
-    "100223",
-    "100224",
-    "100226",
-    "100227",
-    "100229",
-    "100230",
-    "100231",
-    "100232",
-    "100233",
-    "100234",
-    "100235",
-    "100237",
-    "100238",
-    "100239",
-    "100240",
-    "100241",
-    "100242",
-    "100243",
-    "100244",
-    "100245",
-    "100246",
-    "100247",
-    "100248",
-    "100249",
-    "100250",
-    "100252",
-    "100253",
-    "100254",
-    "100255",
-    "100256",
-    "100257",
-    "100258",
-    "100259",
-    "100260",
-    "100261",
-    "100262",
-    "100263",
-    "100264",
-    "100265",
-    "100266",
-    "100268",
-    "100269",
-    "100270",
-    "100271",
-    "100272",
-    "100273",
-    "100274",
-    "100275",
-    "100276",
-    "100277",
-    "100278",
-    "100279",
-    "100280",
-    "100281",
-    "100282",
-    "100283",
-    "100284",
-    "100285",
-]
-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

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