Datasets:
lener_br

	# 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
	#
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	# 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.
	"""LeNER-Br dataset"""


	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """
	@inproceedings{luz_etal_propor2018,
	author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
	Renato R. R. {de Oliveira} and Matheus Stauffer and
	Samuel Couto and Paulo Bermejo},
	title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
	booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},
	publisher = {Springer},
	series = {Lecture Notes on Computer Science ({LNCS})},
	pages = {313--323},
	year = {2018},
	month = {September 24-26},
	address = {Canela, RS, Brazil},
	doi = {10.1007/978-3-319-99722-3_32},
	url = {https://cic.unb.br/~teodecampos/LeNER-Br/},
	}
	"""

	_DESCRIPTION = """
	LeNER-Br is a Portuguese language dataset for named entity recognition
	applied to legal documents. LeNER-Br consists entirely of manually annotated
	legislation and legal cases texts and contains tags for persons, locations,
	time entities, organizations, legislation and legal cases.
	To compose the dataset, 66 legal documents from several Brazilian Courts were
	collected. Courts of superior and state levels were considered, such as Supremo
	Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas
	Gerais and Tribunal de Contas da União. In addition, four legislation documents
	were collected, such as "Lei Maria da Penha", giving a total of 70 documents
	"""

	_HOMEPAGE = "https://cic.unb.br/~teodecampos/LeNER-Br/"

	_URL = "https://github.com/peluz/lener-br/raw/master/leNER-Br/"
	_TRAINING_FILE = "train/train.conll"
	_DEV_FILE = "dev/dev.conll"
	_TEST_FILE = "test/test.conll"


	class LenerBr(datasets.GeneratorBasedBuilder):
	"""LeNER-Br dataset"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="lener_br", version=VERSION, description="LeNER-Br 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-TEMPO",
	"I-TEMPO",
	"B-LOCAL",
	"I-LOCAL",
	"B-LEGISLACAO",
	"I-LEGISLACAO",
	"B-JURISPRUDENCIA",
	"I-JURISPRUDENCIA",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://cic.unb.br/~teodecampos/LeNER-Br/",
	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,
	}