Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Portuguese
Size:
10K<n<100K
File size: 16,377 Bytes
15680f8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 |
'''Ulysses-Ner dataset'''
import datasets
import os
from pathlib import Path
# Também dá para criar uma citação para o C corpus
_CITATION_PL_CORPUS = """
@InProceedings{10.1007\/978-3-030-98305-5_1,
author="Albuquerque, Hidelberg O.
and Costa, Rosimeire
and Silvestre, Gabriel
and Souza, Ellen
and da Silva, N{\\'a}dia F. F.
and Vit{\\'o}rio, Douglas
and Moriyama, Gyovana
and Martins, Lucas
and Soezima, Luiza
and Nunes, Augusto
and Siqueira, Felipe
and Tarrega, Jo{\~a}o P.
and Beinotti, Joao V.
and Dias, Marcio
and Silva, Matheus
and Gardini, Miguel
and Silva, Vinicius
and de Carvalho, Andr{\\'e} C. P. L. F.
and Oliveira, Adriano L. I.",
editor="Pinheiro, Vl{\\'a}dia
and Gamallo, Pablo
and Amaro, Raquel
and Scarton, Carolina
and Batista, Fernando
and Silva, Diego
and Magro, Catarina
and Pinto, Hugo",
title="UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition",
booktitle="Computational Processing of the Portuguese Language",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="3--14",
abstract="The amount of legislative documents produced within the past decade has risen dramatically, making it difficult for law practitioners to consult and update legislation. Named Entity Recognition (NER) systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes. We introduce the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines. The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies. We implemented Conditional Random Field (CRF) and Hidden Markov Model (HMM) models, and the promising F1-score of 80.8{\%} in the analysis by categories and 81.04{\%} in the analysis by types, was achieved with the CRF model. The entities with the best average F1-score results were ``FUNDlei'' and ``DATA'', and the ones with the worst results were ``EVENTO'' and ``PESSOAgrupoind''. The corpus was also evaluated using a BiLSTM-CRF and Glove architectures provided by the pioneering state-of-the-art paper, achieving F1-score of 76.89{\%} in the analysis by categories and 59.67{\%} in the analysis by types.",
isbn="978-3-030-98305-5"
}
"""
_DESCRIPTION = """
The amount of legislative documents produced within the past decade has risen dramatically, making it difficult for law practitioners to consult and update legislation.
Named Entity Recognition (NER) systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes.
We introduce the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines.
The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies.
We implemented Conditional Random Field (CRF) and Hidden Markov Model (HMM) models, and the promising F1-score of 80.8% in the analysis by categories and 81.04 in the analysis by types, was achieved with the CRF model. The entities with the best average F1-score results were “FUNDlei” and “DATA”, and the ones with the worst results were “EVENTO” and “PESSOAgrupoind”. The corpus was also evaluated using a BiLSTM-CRF and Glove architectures provided by the pioneering state-of-the-art paper, achieving F1-score of 76.89% in the analysis by categories and 59.67% in the analysis by types.
Keywords: Annotation Schema · Named Entity Recognition · Legal Information Retrieval.
"""
_URL = 'https://github.com/ulysses-camara/ulysses-ner-br'
_HOMEPAGE = 'https://github.com/ulysses-camara'
_LICENSE = ''
_URLS = {
'pl_corpus_categorias' : {
'train' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/PL_corpus_conll/pl_corpus_categorias/train.txt',
'valid' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/PL_corpus_conll/pl_corpus_categorias/valid.txt',
'test' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/PL_corpus_conll/pl_corpus_categorias/test.txt',
},
'pl_corpus_tipos' : {
'train' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/PL_corpus_conll/pl_corpus_tipos/train.txt',
'valid' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/PL_corpus_conll/pl_corpus_tipos/valid.txt',
'test' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/PL_corpus_conll/pl_corpus_tipos/test.txt',
},
'c_corpus_categorias' : {
'train' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/C_corpus_conll/c_corpus_categorias/train.txt',
'valid' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/C_corpus_conll/c_corpus_categorias/valid.txt',
'test' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/C_corpus_conll/c_corpus_categorias/test.txt',
},
'c_corpus_tipos' : {
'train' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/C_corpus_conll/c_corpus_tipos/train.txt',
'valid' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/C_corpus_conll/c_corpus_tipos/valid.txt',
'test' : 'https://raw.githubusercontent.com/ulysses-camara/ulysses-ner-br/main/annotated-corpora/C_corpus_conll/c_corpus_tipos/test.txt',
},
}
# https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
# https://huggingface.co/docs/datasets/v3.0.0/dataset_script
# https://huggingface.co/docs/datasets/v3.0.0/share
# https://huggingface.co/docs/datasets/v3.0.0/repository_structure
# https://huggingface.co/docs/hub/repositories-pull-requests-discussions#pull-requests-advanced-usage
# https://github.com/ulysses-camara/ulysses-ner-br/tree/main/annotated-corpora
# pl_corpus_categorias
# pl_corpus_tipos
# c_corpus_categorias
# c_corpus_tipos
class UlyssesNerBrConfig(datasets.BuilderConfig):
'''Builder Config for UlyssesNER-Br'''
def __init__(self, features, data_url, citation, url, version, label_classes=("False", "True"), **kwargs):
"""BuilderConfig for UlyssesNER-Br.
Args:
features: *list[string]*, list of the features that will appear in the
feature dict. Should not include "label".
data_url: *string*, url to download the zip file from.
citation: *string*, citation for the data set.
url: *string*, url for information about the data set.
label_classes: *list[string]*, the list of classes for the label if the
label is present as a string. Non-string labels will be cast to either
'False' or 'True'.
**kwargs: keyword arguments forwarded to super.
"""
# Version history:
# 1.0.2: Fixed non-nondeterminism in ReCoRD.
# 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
# the full release (v2.0).
# 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
# 0.0.2: Initial version.
super().__init__(version=version, **kwargs)
self.features = features
self.label_classes = label_classes
self.data_url = data_url
self.citation = citation
self.url = url
class UlyssesNerBr(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
# BUILDER_CONFIG_CLASS = UlyssesNerBrConfig
BUILDER_CONFIG_CLASS = datasets.BuilderConfig
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name='pl_corpus_categorias',
version=VERSION,
description=''
),
datasets.BuilderConfig(
name='pl_corpus_tipos',
version=VERSION,
description=''
),
datasets.BuilderConfig(
name='c_corpus_categorias',
version=VERSION,
description=''
),
datasets.BuilderConfig(
name='c_corpus_tipos',
version=VERSION,
description=''
)
]
DEFAULT_CONFIG_NAME = 'pl_corpus_categorias'
def _info(self):
if self.config.name == 'pl_corpus_categorias' \
or self.config.name == 'c_corpus_categorias' :
features = datasets.Features(
{
'id' : datasets.Value('string'),
'tokens' : datasets.Sequence(datasets.Value('string')),
'ner_tags' : datasets.Sequence(
datasets.features.ClassLabel(
names=[
'O',
'B-DATA',
'I-DATA',
'B-PESSOA',
'I-PESSOA',
'B-ORGANIZACAO',
'I-ORGANIZACAO',
'B-FUNDAMENTO',
'I-FUNDAMENTO',
'B-LOCAL',
'I-LOCAL',
'B-PRODUTODELEI',
'I-PRODUTODELEI',
'B-EVENTO',
'I-EVENTO',
]
)
),
}
)
elif self.config.name == 'pl_corpus_tipos':
features = datasets.Features(
{
'id' : datasets.Value('string'),
'tokens' : datasets.Sequence(datasets.Value('string')),
'ner_tags' : datasets.Sequence(
datasets.features.ClassLabel(
names=[
'O',
'B-DATA',
'I-DATA',
'B-PESSOAgrupocargo',
'I-PESSOAgrupocargo',
'B-PESSOAindividual',
'I-PESSOAindividual',
'B-PESSOAcargo',
'I-PESSOAcargo',
'B-ORGgovernamental',
'I-ORGgovernamental',
'B-ORGnaogovernamental',
'I-ORGnaogovernamental',
'B-ORGpartido',
'I-ORGpartido',
'B-FUNDlei',
'I-FUNDlei',
'B-FUNDprojetodelei',
'I-FUNDprojetodelei',
'B-FUNDapelido',
'I-FUNDapelido',
'B-LOCALconcreto',
'I-LOCALconcreto',
'B-LOCALvirtual',
'I-LOCALvirtual',
'B-PRODUTOprograma',
'I-PRODUTOprograma',
'B-PRODUTOsistema',
'I-PRODUTOsistema',
'B-PRODUTOoutros',
'I-PRODUTOoutros',
'B-EVENTO',
'I-EVENTO',
]
)
),
}
)
elif self.config.name == 'c_corpus_tipos' :
features = datasets.Features(
{
'id' : datasets.Value('string'),
'tokens' : datasets.Sequence(datasets.Value('string')),
'ner_tags' : datasets.Sequence(
datasets.features.ClassLabel(
names=[
'O',
'B-DATA',
'I-DATA',
'B-EVENTO',
'I-EVENTO',
'B-FUNDapelido',
'I-FUNDapelido',
'B-FUNDlei',
'I-FUNDlei',
'B-FUNDprojetodelei',
'I-FUNDprojetodelei',
'B-LOCALconcreto',
'I-LOCALconcreto',
'B-LOCALvirtual',
'I-LOCALvirtual',
'B-ORGgovernamental',
'I-ORGgovernamental',
'B-ORGnaogovernamental',
'I-ORGnaogovernamental',
'B-ORGpartido',
'I-ORGpartido',
'B-PESSOAcargo',
'I-PESSOAcargo',
'B-PESSOAgrupocargo',
'I-PESSOAgrupocargo',
'B-PESSOAgrupoind',
'I-PESSOAgrupoind',
'B-PESSOAindividual',
'I-PESSOAindividual',
'B-PRODUTOoutros',
'I-PRODUTOoutros',
'B-PRODUTOprograma',
'I-PRODUTOprograma',
'B-PRODUTOsistema',
'I-PRODUTOsistema',
]
)
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION_PL_CORPUS
)
def _split_generators(self, dl_manager: datasets.DownloadManager | datasets.StreamingDownloadManager):
'''
'''
urls = _URLS[self.config.name]
data_fir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
'filepath' : data_fir['train'],
'split' : 'train',
}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
'filepath' : data_fir['valid'],
'split' : 'validation',
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
'filepath' : data_fir['test'],
'split' : 'test',
}
),
]
def _generate_examples(self, filepath, split):
guid = 0
sentence = list()
label = list()
with open(filepath, encoding='utf-8') as file :
for line in file:
splited = line.strip()
if line == '' or line == '\n':
if sentence:
yield guid, {
'id' : str(guid),
'tokens' : sentence,
'ner_tags' : label,
}
guid += 1
sentence = list()
label = list()
else:
splited = line.split(' ')
sentence.append(splited[0])
label.append(splited[1])
|