Convert dataset to Parquet (#5)

- Convert dataset to Parquet (064fab99e6683afedddfd4c01239a7ddae8c7266)
- Add ecthr_b data files (352f7328c0b977cc15c0abefadfe17686e934067)
- Add eurlex data files (d3e022f0dc4d2ccc3e39f221b0fd20606860655e)
- Add scotus data files (ffa3e5fdda2e9fc1b5a254ff46e0578440207e02)
- Add ledgar data files (6af614c5ab6d013a02f134e9e9f95373885327e3)
- Add unfair_tos data files (b7b8b5847681813d675d3945a80d2f1b3554be7f)
- Add case_hold data files (fe9d5f8f254c32c0d9c343e86534523deb2fcb01)
- Delete loading script (9c205f32915297e23b9922ab44df1a931823d7fd)
- Delete legacy dataset_infos.json (7ada00fe09504e4c6e7131da2dc8b2684921471b)

README.md

@@ -22,7 +22,42 @@ task_ids:
 - multiple-choice-qa
 - topic-classification
 pretty_name: LexGLUE
+config_names:
+- case_hold
+- ecthr_a
+- ecthr_b
+- eurlex
+- ledgar
+- scotus
+- unfair_tos
 dataset_info:
+- 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: 74781706
+    num_examples: 45000
+  - name: test
+    num_bytes: 5989952
+    num_examples: 3600
+  - name: validation
+    num_bytes: 6474603
+    num_examples: 3900
+  download_size: 47303537
+  dataset_size: 87246261
 - config_name: ecthr_a
   features:
   - name: text
@@ -43,16 +78,16 @@ dataset_info:
           '9': P1-1
   splits:
   - name: train
-    num_bytes: 89637461
+    num_bytes: 89637449
     num_examples: 9000
   - name: test
-    num_bytes: 11884180
+    num_bytes: 11884168
     num_examples: 1000
   - name: validation
-    num_bytes: 10985180
+    num_bytes: 10985168
     num_examples: 1000
-  download_size: 32852475
-  dataset_size: 112506821
+  download_size: 53352586
+  dataset_size: 112506785
 - config_name: ecthr_b
   features:
   - name: text
@@ -73,16 +108,16 @@ dataset_info:
           '9': P1-1
   splits:
   - name: train
-    num_bytes: 89657661
+    num_bytes: 89657649
     num_examples: 9000
   - name: test
-    num_bytes: 11886940
+    num_bytes: 11886928
     num_examples: 1000
   - name: validation
-    num_bytes: 10987828
+    num_bytes: 10987816
     num_examples: 1000
-  download_size: 32852475
-  dataset_size: 112532429
+  download_size: 53352494
+  dataset_size: 112532393
 - config_name: eurlex
   features:
   - name: text
@@ -193,49 +228,16 @@ dataset_info:
           '99': '100285'
   splits:
   - name: train
-    num_bytes: 390770289
+    num_bytes: 390770241
     num_examples: 55000
   - name: test
-    num_bytes: 59739102
+    num_bytes: 59739094
     num_examples: 5000
   - name: validation
-    num_bytes: 41544484
+    num_bytes: 41544476
     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
+  download_size: 208028049
+  dataset_size: 492053811
 - config_name: ledgar
   features:
   - name: text
@@ -346,16 +348,49 @@ dataset_info:
           '99': Withholdings
   splits:
   - name: train
-    num_bytes: 43358315
+    num_bytes: 43358291
     num_examples: 60000
   - name: test
-    num_bytes: 6845585
+    num_bytes: 6845581
     num_examples: 10000
   - name: validation
-    num_bytes: 7143592
+    num_bytes: 7143588
     num_examples: 10000
-  download_size: 16255623
-  dataset_size: 57347492
+  download_size: 27650585
+  dataset_size: 57347460
+- 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: 178959316
+    num_examples: 5000
+  - name: test
+    num_bytes: 76213279
+    num_examples: 1400
+  - name: validation
+    num_bytes: 75600243
+    num_examples: 1400
+  download_size: 173411399
+  dataset_size: 330772838
 - config_name: unfair_tos
   features:
   - name: text
@@ -374,51 +409,73 @@ dataset_info:
           '7': Arbitration
   splits:
   - name: train
-    num_bytes: 1041790
+    num_bytes: 1041782
     num_examples: 5532
   - name: test
-    num_bytes: 303107
+    num_bytes: 303099
     num_examples: 1607
   - name: validation
-    num_bytes: 452119
+    num_bytes: 452111
     num_examples: 2275
-  download_size: 511342
-  dataset_size: 1797016
+  download_size: 865604
+  dataset_size: 1796992
+configs:
 - 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
-config_names:
-- case_hold
-- ecthr_a
-- ecthr_b
-- eurlex
-- ledgar
-- scotus
-- unfair_tos
+  data_files:
+  - split: train
+    path: case_hold/train-*
+  - split: test
+    path: case_hold/test-*
+  - split: validation
+    path: case_hold/validation-*
+- config_name: ecthr_a
+  data_files:
+  - split: train
+    path: ecthr_a/train-*
+  - split: test
+    path: ecthr_a/test-*
+  - split: validation
+    path: ecthr_a/validation-*
+- config_name: ecthr_b
+  data_files:
+  - split: train
+    path: ecthr_b/train-*
+  - split: test
+    path: ecthr_b/test-*
+  - split: validation
+    path: ecthr_b/validation-*
+- config_name: eurlex
+  data_files:
+  - split: train
+    path: eurlex/train-*
+  - split: test
+    path: eurlex/test-*
+  - split: validation
+    path: eurlex/validation-*
+- config_name: ledgar
+  data_files:
+  - split: train
+    path: ledgar/train-*
+  - split: test
+    path: ledgar/test-*
+  - split: validation
+    path: ledgar/validation-*
+- config_name: scotus
+  data_files:
+  - split: train
+    path: scotus/train-*
+  - split: test
+    path: scotus/test-*
+  - split: validation
+    path: scotus/validation-*
+- config_name: unfair_tos
+  data_files:
+  - split: train
+    path: unfair_tos/train-*
+  - split: test
+    path: unfair_tos/test-*
+  - split: validation
+    path: unfair_tos/validation-*
 ---
 
 # Dataset Card for "LexGLUE"

case_hold/test-00000-of-00001.parquet

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+size 3256651

case_hold/train-00000-of-00001.parquet

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+oid sha256:b0c4a7c5f5228d412a09411b6feac17cff791d072efb72d567dd5460ec7a5925
+size 40533452

case_hold/validation-00000-of-00001.parquet

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+size 3513434

dataset_infos.json

@@ -1 +0,0 @@
-{"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": "test", "num_bytes": 11886940, "num_examples": 1000, "dataset_name": "lex_glue"}, "validation": {"name": "validation", "num_bytes": 10987828, "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": 112532429, "size_in_bytes": 145384904}, "eurlex": {"description": "European Union (EU) legislation is published in EUR-Lex portal.\nAll EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus,\na multilingual thesaurus maintained by the Publications Office.\nThe current version of EuroVoc contains more than 7k concepts referring to various activities\nof the EU and its Member States (e.g., economics, health-care, trade).\nGiven a document, the task is to predict its EuroVoc labels (concepts).", "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": null, "_type": "Value"}, "labels": {"feature": {"num_classes": 100, "names": ["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", 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"validation": {"name": "validation", "num_bytes": 41544484, "num_examples": 5000, "dataset_name": "lex_glue"}}, "download_checksums": {"https://zenodo.org/record/5532997/files/eurlex.tar.gz": {"num_bytes": 125413277, "checksum": "82376ff55c3812632d8a21ad0d7e515e2e7ec6431ca7673a454cdd41a3a7bf46"}}, "download_size": 125413277, "post_processing_size": null, "dataset_size": 492053875, "size_in_bytes": 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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lex_glue.py

@@ -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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