Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
multi-class-classification
Languages:
Spanish
Size:
1K<n<10K
Tags:
License:
| # Loading script for the TeCla dataset. | |
| import json | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """ | |
| """ | |
| _DESCRIPTION = """ | |
| WikiCAT: Text Classification Spanish dataset from the Viquipedia | |
| """ | |
| _HOMEPAGE = """ """ | |
| # TODO: upload datasets to github | |
| _URL = "https://huggingface.co/datasets/crodri/WikiCAT_esv2/resolve/main/" | |
| _TRAINING_FILE = "hftrain_esv5.json" | |
| _DEV_FILE = "hfeval_esv5.json" | |
| #_TEST_FILE = "test.json" | |
| class wikiCAT_esConfig(datasets.BuilderConfig): | |
| """ Builder config for the Topicat dataset """ | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for wikiCAT_es. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(wikiCAT_esConfig, self).__init__(**kwargs) | |
| class wikiCAT_es(datasets.GeneratorBasedBuilder): | |
| """ wikiCAT_es Dataset """ | |
| BUILDER_CONFIGS = [ | |
| wikiCAT_esConfig( | |
| name="wikiCAT_es", | |
| version=datasets.Version("1.1.0"), | |
| description="wikiCAT_es", | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "text": datasets.Value("string"), | |
| "label": datasets.features.ClassLabel | |
| (names= ['Religión', 'Entretenimiento', 'Música', 'Ciencia_y_Tecnología', 'Política', 'Economía', 'Matemáticas', 'Humanidades', 'Deporte', 'Derecho', 'Historia', 'Filosofía'] | |
| ), | |
| } | |
| ), | |
| 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: | |
| wikiCAT_es = json.load(f) | |
| for id_, article in enumerate(wikiCAT_es["data"]): | |
| text = article["sentence"] | |
| label = article["label"] | |
| yield id_, { | |
| "text": text, | |
| "label": label, | |
| } | |