Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Languages:
Catalan
Size:
100K - 1M
License:
# Loading script for the TeCla dataset. | |
import json | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """ | |
Carrino, Casimiro Pio, Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021). | |
TeCla: Text Classification Catalan dataset (Version 1.0) [Data set]. | |
Zenodo. http://doi.org/10.5281/zenodo.4627198 | |
""" | |
_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 = "https://huggingface.co/datasets/bsc/tecla/resolve/main/" | |
_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 dataset", | |
), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"text": datasets.Value("string"), | |
"label": datasets.features.ClassLabel | |
(names= | |
[ | |
"Medi ambient", | |
"Societat", | |
"Policial", | |
"Judicial", | |
"Empresa", | |
"Partits", | |
"Pol\u00edtica", | |
"Successos", | |
"Salut", | |
"Infraestructures", | |
"Parlament", | |
"M\u00fasica", | |
"Govern", | |
"Uni\u00f3 Europea", | |
"Economia", | |
"Mobilitat", | |
"Treball", | |
"Cultura", | |
"Educaci\u00f3" | |
] | |
), | |
} | |
), | |
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"] | |
label = article["label"] | |
yield id_, { | |
"text": text, | |
"label": label, | |
} | |