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fairlex.py

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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.
+"""Fairlex: A multilingual benchmark for evaluating fairness in legal text processing."""
+
+import json
+import os
+import textwrap
+
+import datasets
+
+
+MAIN_CITATION = """\
+@inproceedings{chalkidis-etal-2022-fairlex,
+    author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
+            Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
+    title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
+    booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
+    year={2022},
+    address={Dublin, Ireland}
+}
+"""
+
+_DESCRIPTION = """\
+Fairlex: A multilingual benchmark for evaluating fairness in legal text processing.
+"""
+
+ECTHR_ARTICLES = ["2", "3", "5", "6", "8", "9", "10", "11", "14", "P1-1"]
+
+SCDB_ISSUE_AREAS = [
+    "Criminal Procedure",
+    "Civil Rights",
+    "First Amendment",
+    "Due Process",
+    "Privacy",
+    "Attorneys",
+    "Unions",
+    "Economic Activity",
+    "Judicial Power",
+    "Federalism",
+    "Federal Taxation",
+]
+
+FSCS_LABELS = ["dismissal", "approval"]
+
+CAIL_LABELS = ["0", "<=12", "<=36", "<=60", "<=120", ">120"]
+
+
+class FairlexConfig(datasets.BuilderConfig):
+    """BuilderConfig for Fairlex."""
+
+    def __init__(
+        self,
+        label_column,
+        url,
+        data_url,
+        citation,
+        label_classes=None,
+        multi_label=None,
+        attributes=None,
+        **kwargs,
+    ):
+        """BuilderConfig for Fairlex.
+
+        Args:
+          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
+          attributes: `List<string>`, names of the protected attributes
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FairlexConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.label_column = label_column
+        self.label_classes = label_classes
+        self.multi_label = multi_label
+        self.attributes = attributes
+        self.url = url
+        self.data_url = data_url
+        self.citation = citation
+
+
+class Fairlex(datasets.GeneratorBasedBuilder):
+    """Fairlex: A multilingual benchmark for evaluating fairness in legal text processing. Version 1.0"""
+
+    BUILDER_CONFIGS = [
+        FairlexConfig(
+            name="ecthr",
+            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). We use the dataset of Chalkidis et al.
+            (2021), which contains 11K cases from ECtHR's public database. Each case is mapped to articles of the ECHR
+            that were violated (if any). This is a multi-label text classification task. Given the facts of a case,
+            the goal is to predict the ECHR articles that were violated, if any, as decided (ruled) by the court."""
+            ),
+            label_column="labels",
+            label_classes=ECTHR_ARTICLES,
+            multi_label=True,
+            attributes=[
+                ("applicant_age", ["n/a", "<=35", "<=65", ">65"]),
+                ("applicant_gender", ["n/a", "male", "female"]),
+                ("defendant_state", ["C.E. European", "Rest of Europe"]),
+            ],
+            data_url="https://zenodo.org/record/6322643/files/ecthr.zip",
+            url="https://huggingface.co/datasets/ecthr_cases",
+            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",
+            }
+            }"""
+            ),
+        ),
+        FairlexConfig(
+            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. We combine information from SCOTUS opinions with the Supreme Court DataBase (SCDB)
+            (Spaeth, 2020). SCDB provides metadata (e.g., date of publication, decisions, issues, decision directions
+            and many more) for all cases. We consider the available 14 thematic issue areas (e.g, Criminal Procedure,
+            Civil Rights, Economic Activity, etc.). This is a single-label multi-class document classification task.
+            Given the court opinion, the goal is to predict the issue area whose focus is on the subject matter
+            of the controversy (dispute). """
+            ),
+            label_column="label",
+            label_classes=SCDB_ISSUE_AREAS,
+            multi_label=False,
+            attributes=[
+                ("decision_direction", ["conservative", "liberal"]),
+                ("respondent_type", ["other", "person", "organization", "public entity", "facility"]),
+            ],
+            url="http://scdb.wustl.edu/data.php",
+            data_url="https://zenodo.org/record/6322643/files/scotus.zip",
+            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}
+            }"""
+            ),
+        ),
+        FairlexConfig(
+            name="fscs",
+            description=textwrap.dedent(
+                """\
+            The Federal Supreme Court of Switzerland (FSCS) is the last level of appeal in Switzerland and similarly
+            to SCOTUS, the court generally hears only the most controversial or otherwise complex cases which have
+            not been sufficiently well solved by lower courts. The court often focus only on small parts of previous
+            decision, where they discuss possible wrong reasoning by the lower court. The Swiss-Judgment-Predict
+            dataset (Niklaus et al., 2021) contains more than 85K decisions from the FSCS written in one of three
+            languages (50K German, 31K French, 4K Italian) from the years 2000 to 2020. The dataset is not parallel,
+            i.e., all cases are unique and decisions are written only in a single language. The dataset provides labels
+            for a simplified binary (approval, dismissal) classification task. Given the facts of the case, the goal
+            is to predict if the plaintiff's request is valid or partially valid."""
+            ),
+            label_column="label",
+            label_classes=FSCS_LABELS,
+            multi_label=False,
+            attributes=[
+                ("decision_language", ["de", "fr", "it"]),
+                ("legal_area", ["other", "public law", "penal law", "civil law", "social law", "insurance law"]),
+                (
+                    "court_region",
+                    [
+                        "n/a",
+                        "Région lémanique",
+                        "Zürich",
+                        "Espace Mittelland",
+                        "Northwestern Switzerland",
+                        "Eastern Switzerland",
+                        "Central Switzerland",
+                        "Ticino",
+                        "Federation",
+                    ],
+                ),
+            ],
+            url="https://github.com/JoelNiklaus/SwissCourtRulingCorpus",
+            data_url="https://zenodo.org/record/6322643/files/fscs.zip",
+            citation=textwrap.dedent(
+                """\
+            @InProceedings{niklaus-etal-2021-swiss,
+              author = {Niklaus, Joel
+                            and Chalkidis, Ilias
+                            and Stürmer, Matthias},
+              title = {Swiss-Court-Predict: A Multilingual Legal Judgment Prediction Benchmark},
+              booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop},
+              year = {2021},
+              location = {Punta Cana, Dominican Republic},
+            }"""
+            ),
+        ),
+        FairlexConfig(
+            name="cail",
+            description=textwrap.dedent(
+                """\
+            The Supreme People's Court of China (CAIL) is the last level of appeal in China and considers cases that
+            originated from the high people's courts concerning matters of national importance. The Chinese AI and Law
+            challenge (CAIL) dataset (Xiao et al., 2018) is a Chinese legal NLP dataset for judgment prediction and
+            contains over 1m criminal cases. The dataset provides labels for relevant article of criminal code
+            prediction, charge (type of crime) prediction, imprisonment term (period) prediction, and monetary penalty
+            prediction. The updated (soft) version of the CAIL dataset has 104K criminal court cases. The tasks is
+            crime severity prediction task, a multi-class classification task, where given the facts of a case,
+            the goal is to predict how severe was the committed crime with respect to the imprisonment term.
+            We approximate crime severity by the length of imprisonment term, split in 6 clusters
+            (0, >=12, >=36, >=60, >=120, >120 months)."""
+            ),
+            label_column="label",
+            label_classes=CAIL_LABELS,
+            multi_label=False,
+            attributes=[
+                ("defendant_gender", ["male", "female"]),
+                ("court_region", ["Beijing", "Liaoning", "Hunan", "Guangdong", "Sichuan", "Guangxi", "Zhejiang"]),
+            ],
+            url="https://github.com/thunlp/LegalPLMs",
+            data_url="https://zenodo.org/record/6322643/files/cail.zip",
+            citation=textwrap.dedent(
+                """\
+            @article{wang-etal-2021-equality,
+                  title={Equality before the Law: Legal Judgment Consistency Analysis for Fairness},
+                  author={Yuzhong Wang and Chaojun Xiao and Shirong Ma and Haoxi Zhong and Cunchao Tu and Tianyang Zhang and Zhiyuan Liu and Maosong Sun},
+                  year={2021},
+                  journal={Science China  - Information Sciences},
+                  url={https://arxiv.org/abs/2103.13868}
+            }"""
+            ),
+        ),
+    ]
+
+    def _info(self):
+        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)
+        for attribute_name, attribute_groups in self.config.attributes:
+            features[attribute_name] = datasets.ClassLabel(names=attribute_groups)
+        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, "train.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.jsonl"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "val.jsonl"),
+                    "split": "val",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples in the raw (text) form."""
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                example = {
+                    "text": data["text"],
+                    self.config.label_column: data[self.config.label_column],
+                }
+                for attribute_name, _ in self.config.attributes:
+                    example[attribute_name] = data["attributes"][attribute_name]
+                yield id_, example