reldi_sr.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
# limitations under the License.


import os

import datasets


_CITATION = ''
_DESCRIPTION = """The dataset contains 5462 training samples, 711 validation samples and 725 test samples. 
Each sample represents a sentence and includes the following features: sentence ID ('sent_id'), 
list of tokens ('tokens'), list of lemmas ('lemmas'), list of UPOS tags ('upos_tags'), 
list of Multext-East tags ('xpos_tags), list of morphological features ('feats'), 
and list of IOB tags ('iob_tags'), which are encoded as class labels.
"""
_HOMEPAGE = ''
_LICENSE = ''

_URL = 'https://huggingface.co/datasets/classla/reldi_sr/raw/main/data.zip'
_TRAINING_FILE = 'train_all.conllu'
_DEV_FILE = 'dev_all.conllu'
_TEST_FILE = 'test_all.conllu'


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

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

    def _info(self):
        features = datasets.Features(
            {
                'sent_id': datasets.Value('string'),
                'tokens': datasets.Sequence(datasets.Value('string')),
                'norms': datasets.Sequence(datasets.Value('string')),
                'lemmas': datasets.Sequence(datasets.Value('string')),
                'upos_tags': datasets.Sequence(datasets.Value('string')),
                'xpos_tags': datasets.Sequence(datasets.Value('string')),
                'feats': datasets.Sequence(datasets.Value('string')),
                'iob_tags': datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            'I-org',
                            'B-misc',
                            'B-per',
                            'B-deriv-per',
                            'B-org',
                            'B-loc',
                            'I-misc',
                            'I-loc',
                            'I-per',
                            'O',
                            'I-*',
                            'B-*'
                        ]
                    )
                )
            }
        )

        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:
            sent_id = ''
            tokens = []
            norms = []
            lemmas = []
            upos_tags = []
            xpos_tags = []
            feats = []
            iob_tags = []
            data_id = 0
            for line in f:
                if line and not line == '\n':
                    if line.startswith('# sent_id'):
                        if tokens:
                            yield data_id, {
                                'sent_id': sent_id,
                                'tokens': tokens,
                                'norms': norms,
                                'lemmas': lemmas,
                                'upos_tags': upos_tags,
                                'xpos_tags': xpos_tags,
                                'feats': feats,
                                'iob_tags': iob_tags
                            }
                            tokens = []
                            norms = []
                            lemmas = []
                            upos_tags = []
                            xpos_tags = []
                            feats = []
                            iob_tags = []
                            data_id += 1
                        sent_id = line.split(' = ')[1].strip()
                    else:
                        splits = line.split('\t')
                        tokens.append(splits[1].strip())
                        norms.append(splits[2].strip())
                        lemmas.append(splits[3].strip())
                        upos_tags.append(splits[4].strip())
                        xpos_tags.append(splits[5].strip())
                        feats.append(splits[6].strip())
                        iob_tags.append(splits[7].strip())

            yield data_id, {
                'sent_id': sent_id,
                'tokens': tokens,
                'norms': norms,
                'lemmas': lemmas,
                'upos_tags': upos_tags,
                'xpos_tags': xpos_tags,
                'feats': feats,
                'iob_tags': iob_tags
            }