# 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. """The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases.""" import json import os import datasets _CITATION = """\ @InProceedings{chalkidis-et-al-2021-ecthr, title = "Paragraph-level Rationale Extraction through Regularization: A case study on European 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 Annual Conference of the North American Chapter of the Association for Computational Linguistics", year = "2021", address = "Mexico City, Mexico", publisher = "Association for Computational Linguistics" } """ _DESCRIPTION = """\ The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases. """ _HOMEPAGE = "http://archive.org/details/ECtHR-NAACL2021/" _LICENSE = "CC BY-NC-SA (Creative Commons / Attribution-NonCommercial-ShareAlike)" _URLs = { "alleged-violation-prediction": "http://archive.org/download/ECtHR-NAACL2021/dataset.zip", "violation-prediction": "http://archive.org/download/ECtHR-NAACL2021/dataset.zip", } ARTICLES = { "2": "Right to life", "3": "Prohibition of torture", "4": "Prohibition of slavery and forced labour", "5": "Right to liberty and security", "6": "Right to a fair trial", "7": "No punishment without law", "8": "Right to respect for private and family life", "9": "Freedom of thought, conscience and religion", "10": "Freedom of expression", "11": "Freedom of assembly and association", "12": "Right to marry", "13": "Right to an effective remedy", "14": "Prohibition of discrimination", "15": "Derogation in time of emergency", "16": "Restrictions on political activity of aliens", "17": "Prohibition of abuse of rights", "18": "Limitation on use of restrictions on rights", "34": "Individual applications", "38": "Examination of the case", "39": "Friendly settlements", "46": "Binding force and execution of judgments", "P1-1": "Protection of property", "P1-2": "Right to education", "P1-3": "Right to free elections", "P3-1": "Right to free elections", "P4-1": "Prohibition of imprisonment for debt", "P4-2": "Freedom of movement", "P4-3": "Prohibition of expulsion of nationals", "P4-4": "Prohibition of collective expulsion of aliens", "P6-1": "Abolition of the death penalty", "P6-2": "Death penalty in time of war", "P6-3": "Prohibition of derogations", "P7-1": "Procedural safeguards relating to expulsion of aliens", "P7-2": "Right of appeal in criminal matters", "P7-3": "Compensation for wrongful conviction", "P7-4": "Right not to be tried or punished twice", "P7-5": "Equality between spouses", "P12-1": "General prohibition of discrimination", "P13-1": "Abolition of the death penalty", "P13-2": "Prohibition of derogations", "P13-3": "Prohibition of reservations", } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class EcthrCases(datasets.GeneratorBasedBuilder): """The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases.""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="alleged-violation-prediction", version=VERSION, description="This part of the dataset covers alleged violation prediction", ), datasets.BuilderConfig( name="violation-prediction", version=VERSION, description="This part of the dataset covers violation prediction", ), ] DEFAULT_CONFIG_NAME = "alleged-violation-prediction" def _info(self): if self.config.name == "alleged-violation-prediction": features = datasets.Features( { "facts": datasets.features.Sequence(datasets.Value("string")), "labels": datasets.features.Sequence(datasets.Value("string")), "silver_rationales": datasets.features.Sequence(datasets.Value("int32")), "gold_rationales": datasets.features.Sequence(datasets.Value("int32")) # These are the features of your dataset like images, labels ... } ) else: features = datasets.Features( { "facts": datasets.features.Sequence(datasets.Value("string")), "labels": datasets.features.Sequence(datasets.Value("string")), "silver_rationales": datasets.features.Sequence(datasets.Value("int32")) # These are the features of your dataset like images, labels ... } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" my_urls = _URLs[self.config.name] data_dir = dl_manager.download_and_extract(my_urls) 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, "dev.jsonl"), "split": "dev", }, ), ] def _generate_examples( self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """Yields examples as (key, example) tuples.""" with open(filepath, encoding="utf-8") as f: for id_, row in enumerate(f): data = json.loads(row) if self.config.name == "alleged-violation-prediction": yield id_, { "facts": data["facts"], "labels": data["allegedly_violated_articles"], "silver_rationales": data["silver_rationales"], "gold_rationales": data["gold_rationales"], } else: yield id_, { "facts": data["facts"], "labels": data["violated_articles"], "silver_rationales": data["silver_rationales"], }