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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.
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.conllup'
_DEV_FILE = 'dev_all.conllup'
_TEST_FILE = 'test_all.conllup'
_DATA_DIR = 'data'
class ReldiSr(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version('1.0.1')
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',
]
)
)
}
)
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 = os.path.join(dl_manager.download_and_extract(_URL), _DATA_DIR)
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' and not line.startswith('# global.columns'):
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
}
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