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Datasets:
classla
/
hr500k

Tasks:
Other
Modalities:
Text
Sub-tasks:
lemmatization
named-entity-recognition
part-of-speech
Languages:
Croatian
Size:
10K - 100K
Tags:
structure-prediction
normalization
tokenization
Libraries:
Datasets
License:
Dataset card Viewer Files Community
3
hr500k / hr500k.py
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Fix file paths and special lines
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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 hr500k training corpus contains about 500,000 tokens manually annotated on the levels of
tokenisation, sentence segmentation, morphosyntactic tagging, lemmatisation and named entities.
On the sentence level, the dataset contains 20159 training samples, 1963 validation samples and 2672 test samples
across the respective data splits. Each sample represents a sentence and includes the following features:
sentence ID ('sent_id'), sentence text ('text'), list of tokens ('tokens'), list of lemmas ('lemmas'),
list of Multext-East tags ('xpos_tags), list of UPOS tags ('upos_tags'),
list of morphological features ('feats'), and list of IOB tags ('iob_tags'). The 'upos_tags' and 'iob_tags' features
are encoded as class labels.
"""
_HOMEPAGE = 'https://www.clarin.si/repository/xmlui/handle/11356/1183#'
_LICENSE = ''
_URLs = {
'ner': 'https://huggingface.co/datasets/classla/hr500k/raw/main/data_ner.zip',
'upos': 'https://huggingface.co/datasets/classla/hr500k/raw/main/data_ner.zip',
'ud': 'https://huggingface.co/datasets/classla/hr500k/raw/main/data_ud.zip'
}
_DATA_DIRS = {
'ner': 'data_ner',
'upos': 'data_ner',
'ud': 'data_ud'
}
class Hr500K(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version('1.0.1')
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name='upos',
version=VERSION,
description=''
),
datasets.BuilderConfig(
name='ner',
version=VERSION,
description=''
),
datasets.BuilderConfig(
name='ud',
version=VERSION,
description=''
)
]
DEFAULT_CONFIG_NAME = 'ner'
def _info(self):
if self.config.name == "upos":
features = datasets.Features(
{
'sent_id': datasets.Value('string'),
'text': datasets.Value('string'),
'tokens': datasets.Sequence(datasets.Value('string')),
'lemmas': datasets.Sequence(datasets.Value('string')),
'xpos_tags': datasets.Sequence(datasets.Value('string')),
'upos_tags': datasets.Sequence(
datasets.features.ClassLabel(
names=[
'X',
'INTJ',
'VERB',
'PROPN',
'ADV',
'ADJ',
'PUNCT',
'PRON',
'DET',
'NUM',
'SYM',
'SCONJ',
'NOUN',
'AUX',
'PART',
'CCONJ',
'ADP'
]
)
),
'feats': datasets.Sequence(datasets.Value('string')),
'iob_tags': datasets.Sequence(datasets.Value('string'))
}
)
elif self.config.name == "ner":
features = datasets.Features(
{
'sent_id': datasets.Value('string'),
'text': datasets.Value('string'),
'tokens': datasets.Sequence(datasets.Value('string')),
'lemmas': datasets.Sequence(datasets.Value('string')),
'xpos_tags': datasets.Sequence(datasets.Value('string')),
'upos_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-deriv-per',
'I-misc',
'I-loc',
'I-per',
'O'
]
)
)
}
)
else:
features = datasets.Features(
{
'sent_id': datasets.Value('string'),
'text': datasets.Value('string'),
'tokens': datasets.Sequence(datasets.Value('string')),
'lemmas': datasets.Sequence(datasets.Value('string')),
'xpos_tags': datasets.Sequence(datasets.Value('string')),
'upos_tags': datasets.Sequence(datasets.Value('string')),
'feats': datasets.Sequence(datasets.Value('string')),
'iob_tags': datasets.Sequence(datasets.Value('string')),
'uds': datasets.Sequence(
datasets.features.ClassLabel(
names=[
'det', 'aux_pass', 'list', 'cc', 'csubj', 'xcomp', 'nmod', 'dislocated', 'acl', 'fixed',
'obj', 'dep', 'advmod_emph', 'goeswith', 'advmod', 'nsubj', 'punct', 'amod', 'expl_pv',
'mark', 'obl', 'flat_foreign', 'conj', 'compound', 'expl', 'csubj_pass', 'appos',
'case', 'advcl', 'parataxis', 'iobj', 'root', 'cop', 'aux', 'orphan', 'discourse',
'nummod', 'nsubj_pass', 'vocative', 'flat', 'ccomp'
]
)
)
}
)
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(_URLs[self.config.name]), _DATA_DIRS[self.config.name])
if self.config.name == 'ud':
training_file = 'train_ner_ud.conllup'
dev_file = 'dev_ner_ud.conllup'
test_file = 'test_ner_ud.conllup'
else:
training_file = 'train_ner.conllu'
dev_file = 'dev_ner.conllu'
test_file = 'test_ner.conllu'
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):
if self.config.name == 'ud':
with open(filepath, encoding='utf-8') as f:
sent_id = ''
text = ''
tokens = []
lemmas = []
xpos_tags = []
upos_tags = []
feats = []
iob_tags = []
uds = []
data_id = 0
for line in f:
if line and not line == '\n' and not line.startswith('# global.columns'):
if line.startswith('#'):
if line.startswith('# sent_id'):
if tokens:
yield data_id, {
'sent_id': sent_id,
'text': text,
'tokens': tokens,
'lemmas': lemmas,
'upos_tags': upos_tags,
'xpos_tags': xpos_tags,
'feats': feats,
'iob_tags': iob_tags,
'uds': uds
}
tokens = []
lemmas = []
upos_tags = []
xpos_tags = []
feats = []
iob_tags = []
uds = []
data_id += 1
sent_id = line.split(' = ')[1].strip()
elif line.startswith('# text'):
text = line.split(' = ')[1].strip()
elif not line.startswith('_'):
splits = line.split('\t')
tokens.append(splits[1].strip())
lemmas.append(splits[2].strip())
upos_tags.append(splits[3].strip())
xpos_tags.append(splits[4].strip())
feats.append(splits[5].strip())
uds.append(splits[7].strip())
yield data_id, {
'sent_id': sent_id,
'text': text,
'tokens': tokens,
'lemmas': lemmas,
'upos_tags': upos_tags,
'xpos_tags': xpos_tags,
'feats': feats,
'iob_tags': iob_tags,
'uds': uds
}
else:
with open(filepath, encoding='utf-8') as f:
sent_id = ''
text = ''
tokens = []
lemmas = []
xpos_tags = []
upos_tags = []
feats = []
iob_tags = []
data_id = 0
for line in f:
if line and not line == '\n':
if line.startswith('#'):
if line.startswith('# sent_id'):
if tokens:
yield data_id, {
'sent_id': sent_id,
'text': text,
'tokens': tokens,
'lemmas': lemmas,
'upos_tags': upos_tags,
'xpos_tags': xpos_tags,
'feats': feats,
'iob_tags': iob_tags
}
tokens = []
lemmas = []
upos_tags = []
xpos_tags = []
feats = []
iob_tags = []
data_id += 1
sent_id = line.split(' = ')[1].strip()
elif line.startswith('# text'):
text = line.split(' = ')[1].strip()
elif not line.startswith('_'):
splits = line.split('\t')
tokens.append(splits[1].strip())
lemmas.append(splits[2].strip())
upos_tags.append(splits[3].strip())
xpos_tags.append(splits[4].strip())
feats.append(splits[5].strip())
iob_tags.append(splits[9].strip())
yield data_id, {
'sent_id': sent_id,
'text': text,
'tokens': tokens,
'lemmas': lemmas,
'upos_tags': upos_tags,
'xpos_tags': xpos_tags,
'feats': feats,
'iob_tags': iob_tags
}