Datasets:

AUEB-NLP
/

ecthr_cases

@@ -1,199 +0,0 @@
-# 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"],
-                    }