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import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
ALBUQUERQUE2022,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.", title="{UlyssesNER-Br}: A Corpus of Brazilian Legislative Documents for Named Entity Recognition", booktitle="Computational Processing of the Portuguese Language", year="2022", pages="3--14",@inproceedings{inPress, PROPOR2022}
"""
_DESCRIPTION = """
PL-corpus is a Portuguese language dataset for named entity recognition applied to legislative documents. Its parte of the UlyssesBR-corpus, and consists entirely of manually annotated public bills texts (projetos de leis) and contains tags for persons, locations, date entities, organizations, legal foundation and bills.
"""
_HOMEPAGE = "https://github.com/Convenio-Camara-dos-Deputados/ulyssesner-br-propor"
_URL = "https://raw.githubusercontent.com/bergoliveira/assessment-of-deep-learning-models-icann/main/pl-corpus/"
_TRAINING_FILE = "train.conll"
_DEV_FILE = "dev.conll"
_TEST_FILE = "test.conll"
class PlCorpus(datasets.GeneratorBasedBuilder):
"""pL-corpus dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="pl-corpus", version=VERSION, description="PL-corpus dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-ORGANIZACAO",
"I-ORGANIZACAO",
"B-PESSOA",
"I-PESSOA",
"B-DATA",
"I-DATA",
"B-LOCAL",
"I-LOCAL",
"B-FUNDAMENTO",
"I-FUNDAMENTO",
"B-PRODUTODELEI",
"I-PRODUTODELEI",
"B-EVENTO",
"I-EVENTO",
]
)
),
}
),
supervised_keys=None,
homepage="https://github.com/Convenio-Camara-dos-Deputados/ulyssesner-br-propor",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": downloaded_files["train"], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": downloaded_files["test"], "split": "test"},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
if line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
splits = line.split(" ")
tokens.append(splits[0])
ner_tags.append(splits[1].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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