| import datasets |
| import json |
| import os |
| from datasets import Value, Sequence |
|
|
| _CITATION = """\ |
| @inproceedings{chalkidis-etal-2019-neural, |
| title = "Neural Legal Judgment Prediction in {E}nglish", |
| author = "Chalkidis, Ilias and |
| Androutsopoulos, Ion and |
| Aletras, Nikolaos", |
| booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", |
| month = jul, |
| year = "2019", |
| address = "Florence, Italy", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/P19-1424", |
| doi = "10.18653/v1/P19-1424", |
| pages = "4317--4323", |
| } |
| """ |
|
|
| _HOMEPAGE = "https://archive.org/details/ECHR-ACL2019" |
| _DESCRIPTION = """\ |
| The ECHR Cases dataset is designed for experimentation of neural judgment prediction, as in the original 2019 ACL paper "Neural Legal Judgment Prediction in English". |
| """ |
|
|
|
|
| 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", |
| } |
|
|
|
|
| class Echr(datasets.GeneratorBasedBuilder): |
| """ECHR dataset.""" |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig(name="non-anon", data_dir="data"), |
| datasets.BuilderConfig(name="anon", data_dir="data_anon"), |
| ] |
|
|
| def _info(self): |
| features = datasets.Features( |
| { |
| "itemid": Value(dtype="string"), |
| "languageisocode": Value(dtype="string"), |
| "respondent": Value(dtype="string"), |
| "branch": Value(dtype="string"), |
| "date": Value(dtype="int64"), |
| "docname": Value(dtype="string"), |
| "importance": Value(dtype="int64"), |
| "conclusion": Value(dtype="string"), |
| "judges": Value(dtype="string"), |
| "text": Sequence(feature=Value(dtype="string")), |
| "violated_articles": Sequence(feature=Value(dtype="string")), |
| "violated_paragraphs": Sequence(feature=Value(dtype="string")), |
| "violated_bulletpoints": Sequence(feature=Value(dtype="string")), |
| "non_violated_articles": Sequence(feature=Value(dtype="string")), |
| "non_violated_paragraphs": Sequence(feature=Value(dtype="string")), |
| "non_violated_bulletpoints": Sequence(feature=Value(dtype="string")), |
| "violated": Value(dtype="bool"), |
| } |
| ) |
|
|
| return datasets.DatasetInfo( |
| features=features, |
| homepage=_HOMEPAGE, |
| description=_DESCRIPTION, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| path_prefix = self.config.data_dir |
| data_dir = dl_manager.download([os.path.join(path_prefix, f"{f}.jsonl") for f in ["train", "test", "dev"]]) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| |
| gen_kwargs={ |
| "filepath": data_dir[0], |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| |
| gen_kwargs={ |
| "filepath": data_dir[1], |
| "split": "test", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| |
| gen_kwargs={ |
| "filepath": data_dir[2], |
| "split": "dev", |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, filepath, split): |
| with open(filepath, encoding="utf-8") as f: |
| for id_, row in enumerate(f): |
| data = json.loads(row) |
| yield id_, data |
|
|
