copa_hr.py

# 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
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import json
import os

import datasets


_CITATION = """@article{DBLP:journals/corr/abs-2104-09243,
  author    = {Nikola Ljubesic and
               Davor Lauc},
  title     = {BERTi{\'{c}} - The Transformer Language Model for Bosnian, Croatian,
               Montenegrin and Serbian},
  journal   = {CoRR},
  volume    = {abs/2104.09243},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.09243},
  archivePrefix = {arXiv},
}"""
_DESCRIPTION = """The COPA-HR dataset (Choice of plausible alternatives in Croatian) is a translation 
of the English COPA dataset (https://people.ict.usc.edu/~gordon/copa.html) by following the 
XCOPA dataset translation methodology (https://arxiv.org/abs/2005.00333). The dataset consists of 1000 premises 
(My body cast a shadow over the grass), each given a question (What is the cause?), and two choices 
(The sun was rising; The grass was cut), with a label encoding which of the choices is more plausible 
given the annotator or translator (The sun was rising).

The dataset is split into 400 training samples, 100 validation samples, and 500 test samples. It includes the 
following features: 'premise', 'choice1', 'choice2', 'label', 'question', 'changed' (boolean).
"""
_HOMEPAGE = 'https://www.clarin.si/repository/xmlui/handle/11356/1404'
_LICENSE = ''

_URL = 'https://huggingface.co/datasets/classla/copa_hr/raw/main/data.zip'
_TRAINING_FILE = 'train.jsonl'
_DEV_FILE = 'val.jsonl'
_TEST_FILE = 'test.jsonl'


class CopaHr(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version('1.0.0')

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name='copa_hr',
            version=VERSION,
            description=''
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                'premise': datasets.Value('string'),
                'choice1': datasets.Value('string'),
                'choice2': datasets.Value('string'),
                'label': datasets.features.ClassLabel(names=['0', '1']),
                'question': datasets.Value('string'),
                'idx': datasets.Value('int64'),
                'changed': datasets.Value('bool')
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = dl_manager.download_and_extract(_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={
                    'filepath': os.path.join(data_dir, _TRAINING_FILE),
                    'split': 'train'}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={
                    'filepath': os.path.join(data_dir, _DEV_FILE),
                    'split': 'dev'}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={
                    'filepath': os.path.join(data_dir, _TEST_FILE),
                    'split': 'test'}
            ),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding='utf-8') as f:
            for i, line in enumerate(f):
                data = json.loads(line)
                yield i, {
                    'premise': data['premise'],
                    'choice1': data['choice1'],
                    'choice2': data['choice2'],
                    'question': data['question'],
                    'label': str(data['label']),
                    'idx': data['idx'],
                    'changed': data['changed']
                }