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import csv
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import datasets
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_DOWNLOAD_URL = "https://huggingface.co/datasets/cfigueroa/glosas_etiquetadas/resolve/main/datasetNew.csv"
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class GlosasEtiquetadas(datasets.GeneratorBasedBuilder):
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"""Glosas Etiquetadas classification dataset."""
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def _info(self):
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return datasets.DatasetInfo(
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features=datasets.Features(
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{
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"text": datasets.Value("string"),
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"label": datasets.ClassLabel(names = ["LIBRERIA_ACCESORIOS","ALIMENTOS","ASEOPERSONAL","VEHICULOS",
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"BANO","BEBE","BEBIDAS","CARNE","CERVEZA","CONSTRUCCION",
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"DECORACION","DEPORTE","DORMITORIO","ELECTRONICA","EQUIPAJE","FERRETERIA",
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"GRIFERIA","ILUMINACION","JARDIN","JOYERIA","JUGUETERIA","LACTEO","LAVANDERIA",
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"LICORES","LIMPIEZA","MASCOTAS","MENAJE","MOBILIARIO",
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"ORGANIZACION","OUTDOOR","PANADERIA","PISOSMUROS","PUERTASVENTANAS",
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"SERVICIOS","TECNOLOGIA","TERRAZAS","VESTUARIO","VINOS","ZAPATERIA", "ILEGIBLE"] ),
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}
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)
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)
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def _split_generators(self, dl_manager):
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path = dl_manager.download_and_extract(_DOWNLOAD_URL)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path, "is_test": False}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path, "is_test": True}),
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]
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def _generate_examples(self, filepath, is_test, test_size = 0.3):
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"""Generate Glosas Etiquetadas examples."""
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with open(filepath, 'r', encoding='latin-1') as csv_file:
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train_threshold = 40001
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csv_reader = csv.reader(
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csv_file, delimiter=';'
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)
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for id_, row in enumerate(csv_reader):
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if id_ > 0:
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text, label = row
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current_row = id_, {"text": text, "label": int(label)}
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if (id_ < train_threshold) & (not is_test):
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yield current_row
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if (id_ >= train_threshold) & (is_test):
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yield current_row
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