Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-label-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Source Datasets:
original
ArXiv:
License:
File size: 8,606 Bytes
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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.
"""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"],
}
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