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# 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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