Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
Catalan
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
expert-generated
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, | |
| } | |