File size: 13,441 Bytes
a7f96f1 263d8a5 a7f96f1 103a7d7 a7f96f1 6a5ed65 a7f96f1 103a7d7 a7f96f1 103a7d7 a7f96f1 103a7d7 a7f96f1 103a7d7 a7f96f1 103a7d7 a7f96f1 103a7d7 a7f96f1 6ef5980 6a5ed65 103a7d7 82efead 103a7d7 6ef5980 a7f96f1 263d8a5 103a7d7 263d8a5 a7f96f1 263d8a5 103a7d7 263d8a5 a7f96f1 263d8a5 103a7d7 263d8a5 a7f96f1 103a7d7 6a5ed65 103a7d7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 |
# 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
}
|