Convert dataset to Parquet (#1)

- Convert dataset to Parquet (1c97af87a2b3e79224d1dd8bd6a719d895aa8ba9)
- Add 'case_hold' config data files (e8c5a20665eca7bc1a38ef423a40044a1c324725)
- Add 'ecthr_a' config data files (bf3f2857c981f49068f00398b168c3cf3b7d553b)
- Add 'ecthr_b' config data files (630da389ee945a4aef939df3ba4bd70540b7452a)
- Add 'eurlex' config data files (0fb195adf1b8903c9a69fe707353ff03b54ee8a7)
- Add 'ledgar' config data files (d7629d2fb1a863e56029d345789c666ce7dbdf99)
- Add 'scotus' config data files (aba7f75dc504dcf1289a0390b6bb3db2ae0fa4a4)
- Add 'unfair_tos' config data files (7c31aacd4a9af1cae78dd23cf40f44f209396883)
- Delete loading script (fa45937b82b79fcd6fc8d6439c057223068078f7)
- Delete data file (939b80d56ab26c423ce9e69bec123e1d1c5007e6)
- Delete data file (0d4b3736195b09953df0f0799c702743b61d1760)
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README.md

@@ -0,0 +1,228 @@
+---
+dataset_info:
+- config_name: all
+  features:
+  - name: input
+    dtype: string
+  - name: references
+    sequence: string
+  - name: gold
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 911549030
+    num_examples: 188532
+  - name: validation
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+    num_examples: 24575
+  - name: test
+    num_bytes: 179174253
+    num_examples: 23607
+  download_size: 567419419
+  dataset_size: 1250276940
+- config_name: case_hold
+  features:
+  - name: input
+    dtype: string
+  - name: references
+    sequence: string
+  - name: gold
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 76401706
+    num_examples: 45000
+  - name: validation
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+    num_examples: 3900
+  - name: test
+    num_bytes: 6119552
+    num_examples: 3600
+  download_size: 48281911
+  dataset_size: 89136261
+- config_name: ecthr_a
+  features:
+  - name: input
+    dtype: string
+  - name: references
+    sequence: string
+  - name: gold
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 89562863
+    num_examples: 9000
+  - name: validation
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+    num_examples: 1000
+  - name: test
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+    num_examples: 1000
+  download_size: 52346441
+  dataset_size: 112410579
+- config_name: ecthr_b
+  features:
+  - name: input
+    dtype: string
+  - name: references
+    sequence: string
+  - name: gold
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 89566128
+    num_examples: 9000
+  - name: validation
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+    num_examples: 1000
+  - name: test
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+    num_examples: 1000
+  download_size: 52347621
+  dataset_size: 112413780
+- config_name: eurlex
+  features:
+  - name: input
+    dtype: string
+  - name: references
+    sequence: string
+  - name: gold
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 402315088
+    num_examples: 55000
+  - name: validation
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+    num_examples: 5000
+  - name: test
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+    num_examples: 5000
+  download_size: 210059741
+  dataset_size: 505717411
+- config_name: ledgar
+  features:
+  - name: input
+    dtype: string
+  - name: references
+    sequence: string
+  - name: gold
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 72965170
+    num_examples: 60000
+  - name: validation
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+    num_examples: 10000
+  - name: test
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+    num_examples: 10000
+  download_size: 29866590
+  dataset_size: 96826772
+- config_name: scotus
+  features:
+  - name: input
+    dtype: string
+  - name: references
+    sequence: string
+  - name: gold
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 179329783
+    num_examples: 5000
+  - name: validation
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+    num_examples: 1400
+  - name: test
+    num_bytes: 76317017
+    num_examples: 1400
+  download_size: 173457949
+  dataset_size: 331350788
+- config_name: unfair_tos
+  features:
+  - name: input
+    dtype: string
+  - name: references
+    sequence: string
+  - name: gold
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 1408292
+    num_examples: 5532
+  - name: validation
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+    num_examples: 2275
+  - name: test
+    num_bytes: 409753
+    num_examples: 1607
+  download_size: 906648
+  dataset_size: 2421349
+configs:
+- config_name: all
+  data_files:
+  - split: train
+    path: all/train-*
+  - split: validation
+    path: all/validation-*
+  - split: test
+    path: all/test-*
+- config_name: case_hold
+  data_files:
+  - split: train
+    path: case_hold/train-*
+  - split: validation
+    path: case_hold/validation-*
+  - split: test
+    path: case_hold/test-*
+- config_name: ecthr_a
+  data_files:
+  - split: train
+    path: ecthr_a/train-*
+  - split: validation
+    path: ecthr_a/validation-*
+  - split: test
+    path: ecthr_a/test-*
+- config_name: ecthr_b
+  data_files:
+  - split: train
+    path: ecthr_b/train-*
+  - split: validation
+    path: ecthr_b/validation-*
+  - split: test
+    path: ecthr_b/test-*
+- config_name: eurlex
+  data_files:
+  - split: train
+    path: eurlex/train-*
+  - split: validation
+    path: eurlex/validation-*
+  - split: test
+    path: eurlex/test-*
+- config_name: ledgar
+  data_files:
+  - split: train
+    path: ledgar/train-*
+  - split: validation
+    path: ledgar/validation-*
+  - split: test
+    path: ledgar/test-*
+- config_name: scotus
+  data_files:
+  - split: train
+    path: scotus/train-*
+  - split: validation
+    path: scotus/validation-*
+  - split: test
+    path: scotus/test-*
+- config_name: unfair_tos
+  data_files:
+  - split: train
+    path: unfair_tos/train-*
+  - split: validation
+    path: unfair_tos/validation-*
+  - split: test
+    path: unfair_tos/test-*
+---

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lexglue.py

@@ -1,572 +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
-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",
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-    "100207",
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-    "100223",
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-    "100241",
-    "100242",
-    "100243",
-    "100244",
-    "100245",
-    "100246",
-    "100247",
-    "100248",
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-    "100252",
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-    "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)

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