Datasets:

projecte-aina
/

tecla

	# Loading script for the TeCla dataset.
	import json
	import datasets

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """
	Baucells, Irene, Carrino, Casimiro Pio, Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021).
	TeCla: Text Classification Catalan dataset (Version 2.0) [Data set].
	Zenodo. http://doi.org/10.5281/zenodo.7334110
	"""

	_DESCRIPTION = """
	TeCla: Text Classification Catalan dataset
	Catalan News corpus for Text classification, crawled from ACN (Catalan News Agency) site: www.acn.cat
	Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies - www.acn.cat
	"""

	_HOMEPAGE = """https://zenodo.org/record/4761505"""

	# TODO: upload datasets to github
	_URL = "./"
	_TRAINING_FILE = "train.json"
	_DEV_FILE = "dev.json"
	_TEST_FILE = "test.json"


	class teclaConfig(datasets.BuilderConfig):
	""" Builder config for the TeCla dataset """

	def __init__(self, **kwargs):
	"""BuilderConfig for TeCla.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(teclaConfig, self).__init__(**kwargs)


	class tecla(datasets.GeneratorBasedBuilder):
	""" TeCla Dataset """

	BUILDER_CONFIGS = [
	teclaConfig(
	name="tecla",
	version=datasets.Version("1.0.1"),
	description="tecla 2.0 dataset",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"label1": datasets.features.ClassLabel
	(names=
	[
	"Societat",
	"Pol\u00edtica",
	"Economia",
	"Cultura",
	]
	),
	"label2": datasets.features.ClassLabel
	(names=
	[
	"Llengua",
	"Infraestructures",
	"Arts",
	"Parlament",
	"Noves tecnologies",
	"Castells",
	"Successos",
	"Empresa",
	"Mobilitat",
	"Teatre",
	"Treball",
	"Log\u00edstica",
	"Urbanisme",
	"Govern",
	"Entitats",
	"Finances",
	"Govern espanyol",
	"Tr\u00e0nsit",
	"Ind\u00fastria",
	"Esports",
	"Exteriors",
	"Medi ambient",
	"Habitatge",
	"Salut",
	"Equipaments i patrimoni",
	"Recerca",
	"Cooperaci\u00f3",
	"Innovaci\u00f3",
	"Agroalimentaci\u00f3",
	"Policial",
	"Serveis Socials",
	"Cinema",
	"Mem\u00f2ria hist\u00f2rica",
	"Turisme",
	"Pol\u00edtica municipal",
	"Comer\u00e7",
	"Universitats",
	"Hisenda",
	"Judicial",
	"Partits",
	"M\u00fasica",
	"Lletres",
	"Religi\u00f3",
	"Festa i cultura popular",
	"Uni\u00f3 Europea",
	"Moda",
	"Moviments socials",
	"Comptes p\u00fablics",
	"Immigraci\u00f3",
	"Educaci\u00f3",
	"Gastronomia",
	"Meteorologia",
	"Energia"
	]
	),
	}
	),
	homepage=_HOMEPAGE,
	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"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	acn_ca = json.load(f)
	for id_, article in enumerate(acn_ca["data"]):
	text = article["sentence"]
	label1 = article["label1"]
	label2 = article["label2"]
	yield id_, {
	"text": text,
	"label1": label1,
	"label2": label2,
	}