init

lexglue.py

@@ -0,0 +1,572 @@
+# 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
+import os
+
+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,
+        url,
+        data_url,
+        data_file,
+        citation,
+        **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.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="all",
+            description="",
+            data_url="",
+            data_file="",
+            url="",
+            citation=""
+            ),
+        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)."""
+            ),
+            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)."""
+            ),
+            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)."""
+            ),
+            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)."""
+            ),
+            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."""
+            ),
+            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."""
+            ),
+            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."""
+            ),
+            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):
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=datasets.Features({
+                "input": datasets.Value("string"),
+                "references": datasets.features.Sequence(datasets.Value("string")),
+                "gold": datasets.features.Sequence(datasets.Value("string"))
+
+            }),
+            homepage=self.config.url,
+            citation=self.config.citation + "\n" + MAIN_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.name == "all":
+            test = [dl_manager.download(os.path.join(name, "test.jsonl")) for name in ["ecthr_a", "ecthr_b", "scotus", "eurlex", "ledgar", "unfair_tos", "case_hold"]]
+            train = [dl_manager.download(os.path.join(name, "train.jsonl")) for name in ["ecthr_a", "ecthr_b", "scotus", "eurlex", "ledgar", "unfair_tos", "case_hold"]]
+            val = [dl_manager.download(os.path.join(name, "validation.jsonl")) for name in ["ecthr_a", "ecthr_b", "scotus", "eurlex", "ledgar", "unfair_tos", "case_hold"]]
+        else:
+            test = [dl_manager.download(os.path.join(self.config.name, "test.jsonl"))]
+            train = [dl_manager.download(os.path.join(self.config.name, "train.jsonl"))]
+            val = [dl_manager.download(os.path.join(self.config.name, "validation.jsonl"))]
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"files": train},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"files": val},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"files": test},
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        """This function returns the examples in the raw (text) form."""
+        for file in files:
+            with open(file, "r") as f:
+                for ix, line in enumerate(f):
+                    yield ix, json.loads(line)