Datasets:
ecthr_cases

	# 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"],
	}