Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
extended
ArXiv:
Tags:
License:
# 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 os | |
import textwrap | |
import datasets | |
MAIN_CITATION = """\ | |
@article{chalkidis-etal-2021-lexglue, | |
title={{LexGLUE}: A Benchmark Dataset for Legal Language Understanding in English}, | |
author={Chalkidis, Ilias and | |
Jana, Abhik and | |
Hartung, Dirk and | |
Bommarito, Michael and | |
Androutsopoulos, Ion and | |
Katz, Daniel Martin and | |
Aletras, Nikolaos}, | |
year={2021}, | |
eprint={2110.00976}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL}, | |
note = {arXiv: 2110.00976}, | |
}""" | |
_DESCRIPTION = """\ | |
Legal General Language Understanding Evaluation (LexGLUE) benchmark is | |
a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks | |
""" | |
ECTHR_ARTICLES = ["2", "3", "5", "6", "8", "9", "10", "11", "14", "P1-1"] | |
EUROVOC_CONCEPTS = [ | |
"100163", | |
"100164", | |
"100165", | |
"100166", | |
"100167", | |
"100168", | |
"100169", | |
"100170", | |
"100171", | |
"100172", | |
"100173", | |
"100174", | |
"100175", | |
"100176", | |
"100177", | |
"100178", | |
"100179", | |
"100180", | |
"100181", | |
"100182", | |
"100183", | |
"100184", | |
"100185", | |
"100186", | |
"100187", | |
"100188", | |
"100189", | |
"100190", | |
"100191", | |
"100192", | |
"100193", | |
"100194", | |
"100195", | |
"100196", | |
"100197", | |
"100198", | |
"100199", | |
"100200", | |
"100201", | |
"100202", | |
"100203", | |
"100204", | |
"100205", | |
"100206", | |
"100207", | |
"100208", | |
"100209", | |
"100210", | |
"100211", | |
"100212", | |
"100213", | |
"100214", | |
"100215", | |
"100216", | |
"100217", | |
"100218", | |
"100219", | |
"100220", | |
"100221", | |
"100222", | |
"100223", | |
"100224", | |
"100225", | |
"100226", | |
"100227", | |
"100228", | |
"100229", | |
"100230", | |
"100231", | |
"100232", | |
"100233", | |
"100234", | |
"100235", | |
"100236", | |
"100237", | |
"100238", | |
"100239", | |
"100240", | |
"100241", | |
"100242", | |
"100243", | |
"100244", | |
"100245", | |
"100246", | |
"100247", | |
"100248", | |
"100249", | |
"100250", | |
"100251", | |
"100252", | |
"100253", | |
"100254", | |
"100255", | |
"100256", | |
"100257", | |
"100258", | |
"100259", | |
"100260", | |
"100261", | |
"100262", | |
"100263", | |
"100264", | |
"100265", | |
"100266", | |
"100267", | |
"100268", | |
"100269", | |
"100270", | |
"100271", | |
"100272", | |
"100273", | |
"100274", | |
"100275", | |
"100276", | |
"100277", | |
"100278", | |
"100279", | |
"100280", | |
"100281", | |
"100282", | |
"100283", | |
"100284", | |
"100285", | |
"100286", | |
"100287", | |
"100288", | |
"100289", | |
] | |
LEDGAR_CATEGORIES = [ | |
"Adjustments", | |
"Agreements", | |
"Amendments", | |
"Anti-Corruption Laws", | |
"Applicable Laws", | |
"Approvals", | |
"Arbitration", | |
"Assignments", | |
"Assigns", | |
"Authority", | |
"Authorizations", | |
"Base Salary", | |
"Benefits", | |
"Binding Effects", | |
"Books", | |
"Brokers", | |
"Capitalization", | |
"Change In Control", | |
"Closings", | |
"Compliance With Laws", | |
"Confidentiality", | |
"Consent To Jurisdiction", | |
"Consents", | |
"Construction", | |
"Cooperation", | |
"Costs", | |
"Counterparts", | |
"Death", | |
"Defined Terms", | |
"Definitions", | |
"Disability", | |
"Disclosures", | |
"Duties", | |
"Effective Dates", | |
"Effectiveness", | |
"Employment", | |
"Enforceability", | |
"Enforcements", | |
"Entire Agreements", | |
"Erisa", | |
"Existence", | |
"Expenses", | |
"Fees", | |
"Financial Statements", | |
"Forfeitures", | |
"Further Assurances", | |
"General", | |
"Governing Laws", | |
"Headings", | |
"Indemnifications", | |
"Indemnity", | |
"Insurances", | |
"Integration", | |
"Intellectual Property", | |
"Interests", | |
"Interpretations", | |
"Jurisdictions", | |
"Liens", | |
"Litigations", | |
"Miscellaneous", | |
"Modifications", | |
"No Conflicts", | |
"No Defaults", | |
"No Waivers", | |
"Non-Disparagement", | |
"Notices", | |
"Organizations", | |
"Participations", | |
"Payments", | |
"Positions", | |
"Powers", | |
"Publicity", | |
"Qualifications", | |
"Records", | |
"Releases", | |
"Remedies", | |
"Representations", | |
"Sales", | |
"Sanctions", | |
"Severability", | |
"Solvency", | |
"Specific Performance", | |
"Submission To Jurisdiction", | |
"Subsidiaries", | |
"Successors", | |
"Survival", | |
"Tax Withholdings", | |
"Taxes", | |
"Terminations", | |
"Terms", | |
"Titles", | |
"Transactions With Affiliates", | |
"Use Of Proceeds", | |
"Vacations", | |
"Venues", | |
"Vesting", | |
"Waiver Of Jury Trials", | |
"Waivers", | |
"Warranties", | |
"Withholdings", | |
] | |
SCDB_ISSUE_AREAS = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"] | |
UNFAIR_CATEGORIES = [ | |
"Limitation of liability", | |
"Unilateral termination", | |
"Unilateral change", | |
"Content removal", | |
"Contract by using", | |
"Choice of law", | |
"Jurisdiction", | |
"Arbitration", | |
] | |
CASEHOLD_LABELS = ["0", "1", "2", "3", "4"] | |
class LexGlueConfig(datasets.BuilderConfig): | |
"""BuilderConfig for LexGLUE.""" | |
def __init__( | |
self, | |
text_column, | |
label_column, | |
url, | |
data_url, | |
data_file, | |
citation, | |
label_classes=None, | |
multi_label=None, | |
dev_column="dev", | |
**kwargs, | |
): | |
"""BuilderConfig for LexGLUE. | |
Args: | |
text_column: ``string`, name of the column in the jsonl file corresponding | |
to the text | |
label_column: `string`, name of the column in the jsonl file corresponding | |
to the label | |
url: `string`, url for the original project | |
data_url: `string`, url to download the zip file from | |
data_file: `string`, filename for data set | |
citation: `string`, citation for the data set | |
url: `string`, url for information about the data set | |
label_classes: `list[string]`, the list of classes if the label is | |
categorical. If not provided, then the label will be of type | |
`datasets.Value('float32')`. | |
multi_label: `boolean`, True if the task is multi-label | |
dev_column: `string`, name for the development subset | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(LexGlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |
self.text_column = text_column | |
self.label_column = label_column | |
self.label_classes = label_classes | |
self.multi_label = multi_label | |
self.dev_column = dev_column | |
self.url = url | |
self.data_url = data_url | |
self.data_file = data_file | |
self.citation = citation | |
class LexGLUE(datasets.GeneratorBasedBuilder): | |
"""LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. Version 1.0""" | |
BUILDER_CONFIGS = [ | |
LexGlueConfig( | |
name="ecthr_a", | |
description=textwrap.dedent( | |
"""\ | |
The European Court of Human Rights (ECtHR) hears allegations that a state has | |
breached human rights provisions of the European Convention of Human Rights (ECHR). | |
For each case, the dataset provides a list of factual paragraphs (facts) from the case description. | |
Each case is mapped to articles of the ECHR that were violated (if any).""" | |
), | |
text_column="facts", | |
label_column="violated_articles", | |
label_classes=ECTHR_ARTICLES, | |
multi_label=True, | |
dev_column="dev", | |
data_url="https://zenodo.org/record/5532997/files/ecthr.tar.gz", | |
data_file="ecthr.jsonl", | |
url="https://archive.org/details/ECtHR-NAACL2021", | |
citation=textwrap.dedent( | |
"""\ | |
@inproceedings{chalkidis-etal-2021-paragraph, | |
title = "Paragraph-level Rationale Extraction through Regularization: A case study on {E}uropean Court of Human Rights Cases", | |
author = "Chalkidis, Ilias and | |
Fergadiotis, Manos and | |
Tsarapatsanis, Dimitrios and | |
Aletras, Nikolaos and | |
Androutsopoulos, Ion and | |
Malakasiotis, Prodromos", | |
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
month = jun, | |
year = "2021", | |
address = "Online", | |
publisher = "Association for Computational Linguistics", | |
url = "https://aclanthology.org/2021.naacl-main.22", | |
doi = "10.18653/v1/2021.naacl-main.22", | |
pages = "226--241", | |
} | |
}""" | |
), | |
), | |
LexGlueConfig( | |
name="ecthr_b", | |
description=textwrap.dedent( | |
"""\ | |
The European Court of Human Rights (ECtHR) hears allegations that a state has | |
breached human rights provisions of the European Convention of Human Rights (ECHR). | |
For each case, the dataset provides a list of factual paragraphs (facts) from the case description. | |
Each case is mapped to articles of ECHR that were allegedly violated (considered by the court).""" | |
), | |
text_column="facts", | |
label_column="allegedly_violated_articles", | |
label_classes=ECTHR_ARTICLES, | |
multi_label=True, | |
dev_column="dev", | |
url="https://archive.org/details/ECtHR-NAACL2021", | |
data_url="https://zenodo.org/record/5532997/files/ecthr.tar.gz", | |
data_file="ecthr.jsonl", | |
citation=textwrap.dedent( | |
"""\ | |
@inproceedings{chalkidis-etal-2021-paragraph, | |
title = "Paragraph-level Rationale Extraction through Regularization: A case study on {E}uropean Court of Human Rights Cases", | |
author = "Chalkidis, Ilias | |
and Fergadiotis, Manos | |
and Tsarapatsanis, Dimitrios | |
and Aletras, Nikolaos | |
and Androutsopoulos, Ion | |
and Malakasiotis, Prodromos", | |
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
year = "2021", | |
address = "Online", | |
url = "https://aclanthology.org/2021.naacl-main.22", | |
} | |
}""" | |
), | |
), | |
LexGlueConfig( | |
name="eurlex", | |
description=textwrap.dedent( | |
"""\ | |
European Union (EU) legislation is published in EUR-Lex portal. | |
All EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, | |
a multilingual thesaurus maintained by the Publications Office. | |
The current version of EuroVoc contains more than 7k concepts referring to various activities | |
of the EU and its Member States (e.g., economics, health-care, trade). | |
Given a document, the task is to predict its EuroVoc labels (concepts).""" | |
), | |
text_column="text", | |
label_column="labels", | |
label_classes=EUROVOC_CONCEPTS, | |
multi_label=True, | |
dev_column="dev", | |
url="https://zenodo.org/record/5363165#.YVJOAi8RqaA", | |
data_url="https://zenodo.org/record/5532997/files/eurlex.tar.gz", | |
data_file="eurlex.jsonl", | |
citation=textwrap.dedent( | |
"""\ | |
@inproceedings{chalkidis-etal-2021-multieurlex, | |
author = {Chalkidis, Ilias and | |
Fergadiotis, Manos and | |
Androutsopoulos, Ion}, | |
title = {MultiEURLEX -- A multi-lingual and multi-label legal document | |
classification dataset for zero-shot cross-lingual transfer}, | |
booktitle = {Proceedings of the 2021 Conference on Empirical Methods | |
in Natural Language Processing}, | |
year = {2021}, | |
location = {Punta Cana, Dominican Republic}, | |
} | |
}""" | |
), | |
), | |
LexGlueConfig( | |
name="scotus", | |
description=textwrap.dedent( | |
"""\ | |
The US Supreme Court (SCOTUS) is the highest federal court in the United States of America | |
and generally hears only the most controversial or otherwise complex cases which have not | |
been sufficiently well solved by lower courts. This is a single-label multi-class classification | |
task, where given a document (court opinion), the task is to predict the relevant issue areas. | |
The 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute).""" | |
), | |
text_column="text", | |
label_column="issueArea", | |
label_classes=SCDB_ISSUE_AREAS, | |
multi_label=False, | |
dev_column="dev", | |
url="http://scdb.wustl.edu/data.php", | |
data_url="https://zenodo.org/record/5532997/files/scotus.tar.gz", | |
data_file="scotus.jsonl", | |
citation=textwrap.dedent( | |
"""\ | |
@misc{spaeth2020, | |
author = {Harold J. Spaeth and Lee Epstein and Andrew D. Martin, Jeffrey A. Segal | |
and Theodore J. Ruger and Sara C. Benesh}, | |
year = {2020}, | |
title ={{Supreme Court Database, Version 2020 Release 01}}, | |
url= {http://Supremecourtdatabase.org}, | |
howpublished={Washington University Law} | |
}""" | |
), | |
), | |
LexGlueConfig( | |
name="ledgar", | |
description=textwrap.dedent( | |
"""\ | |
LEDGAR dataset aims contract provision (paragraph) classification. | |
The contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) | |
filings, which are publicly available from EDGAR. Each label represents the single main topic | |
(theme) of the corresponding contract provision.""" | |
), | |
text_column="text", | |
label_column="clause_type", | |
label_classes=LEDGAR_CATEGORIES, | |
multi_label=False, | |
dev_column="dev", | |
url="https://metatext.io/datasets/ledgar", | |
data_url="https://zenodo.org/record/5532997/files/ledgar.tar.gz", | |
data_file="ledgar.jsonl", | |
citation=textwrap.dedent( | |
"""\ | |
@inproceedings{tuggener-etal-2020-ledgar, | |
title = "{LEDGAR}: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts", | |
author = {Tuggener, Don and | |
von D{\"a}niken, Pius and | |
Peetz, Thomas and | |
Cieliebak, Mark}, | |
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", | |
year = "2020", | |
address = "Marseille, France", | |
url = "https://aclanthology.org/2020.lrec-1.155", | |
} | |
}""" | |
), | |
), | |
LexGlueConfig( | |
name="unfair_tos", | |
description=textwrap.dedent( | |
"""\ | |
The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, | |
Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of | |
unfair contractual terms (sentences), meaning terms that potentially violate user rights | |
according to the European consumer law.""" | |
), | |
text_column="text", | |
label_column="labels", | |
label_classes=UNFAIR_CATEGORIES, | |
multi_label=True, | |
dev_column="val", | |
url="http://claudette.eui.eu", | |
data_url="https://zenodo.org/record/5532997/files/unfair_tos.tar.gz", | |
data_file="unfair_tos.jsonl", | |
citation=textwrap.dedent( | |
"""\ | |
@article{lippi-etal-2019-claudette, | |
title = "{CLAUDETTE}: an automated detector of potentially unfair clauses in online terms of service", | |
author = {Lippi, Marco | |
and Pałka, Przemysław | |
and Contissa, Giuseppe | |
and Lagioia, Francesca | |
and Micklitz, Hans-Wolfgang | |
and Sartor, Giovanni | |
and Torroni, Paolo}, | |
journal = "Artificial Intelligence and Law", | |
year = "2019", | |
publisher = "Springer", | |
url = "https://doi.org/10.1007/s10506-019-09243-2", | |
pages = "117--139", | |
}""" | |
), | |
), | |
LexGlueConfig( | |
name="case_hold", | |
description=textwrap.dedent( | |
"""\ | |
The CaseHOLD (Case Holdings on Legal Decisions) dataset contains approx. 53k multiple choice | |
questions about holdings of US court cases from the Harvard Law Library case law corpus. | |
Holdings are short summaries of legal rulings accompany referenced decisions relevant for the present case. | |
The input consists of an excerpt (or prompt) from a court decision, containing a reference | |
to a particular case, while the holding statement is masked out. The model must identify | |
the correct (masked) holding statement from a selection of five choices.""" | |
), | |
text_column="text", | |
label_column="labels", | |
dev_column="dev", | |
multi_label=False, | |
label_classes=CASEHOLD_LABELS, | |
url="https://github.com/reglab/casehold", | |
data_url="https://zenodo.org/record/5532997/files/casehold.tar.gz", | |
data_file="casehold.csv", | |
citation=textwrap.dedent( | |
"""\ | |
@inproceedings{Zheng2021, | |
author = {Lucia Zheng and | |
Neel Guha and | |
Brandon R. Anderson and | |
Peter Henderson and | |
Daniel E. Ho}, | |
title = {When Does Pretraining Help? Assessing Self-Supervised Learning for | |
Law and the CaseHOLD Dataset}, | |
year = {2021}, | |
booktitle = {International Conference on Artificial Intelligence and Law}, | |
}""" | |
), | |
), | |
] | |
def _info(self): | |
if self.config.name == "case_hold": | |
features = { | |
"question": datasets.Value("string"), | |
"contexts": datasets.features.Sequence(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): | |
data_dir = dl_manager.download_and_extract(self.config.data_url) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(data_dir, self.config.data_file), "split": "train"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(data_dir, self.config.data_file), "split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, self.config.data_file), | |
"split": self.config.dev_column, | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""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)} | |
with open(filepath, "r", encoding="utf-8") as f: | |
for id_, row in enumerate(list(csv.reader(f))[SPLIT_RANGES[split][0] : SPLIT_RANGES[split][1]]): | |
yield id_, { | |
"context": row[1], | |
"holdings": [row[2], row[3], row[4], row[5], row[6]], | |
"label": str(row[12]), | |
} | |
elif self.config.multi_label: | |
with open(filepath, "r", encoding="utf-8") as f: | |
for id_, row in enumerate(f): | |
data = json.loads(row) | |
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, | |
} | |
else: | |
with open(filepath, "r", encoding="utf-8") as f: | |
for id_, row in enumerate(f): | |
data = json.loads(row) | |
if data["data_type"] == split: | |
yield id_, { | |
"text": data[self.config.text_column], | |
"label": data[self.config.label_column], | |
} | |