|
import csv |
|
import datasets |
|
|
|
_DOWNLOAD_URL = "https://huggingface.co/datasets/cfigueroa/glosas_etiquetadas/resolve/main/dataset2.csv" |
|
|
|
class GlosasEtiquetadas(datasets.GeneratorBasedBuilder): |
|
"""Glosas Etiquetadas classification dataset.""" |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
features=datasets.Features( |
|
{ |
|
"glosa": datasets.Value("string"), |
|
"categoria": datasets.ClassLabel(names = ["ACCESORIOS","ALIMENTOS","ANTEOJOS","ASEOPERSONAL","AUTOMOTOR","BANO","BEBE","BEBIDAS","BICICLETAS", |
|
"CALEFONT","CARNE","CERVEZA","CONSTRUCCION","DECORACION","DEPORTE","DORMITORIO","ELECTRONICA","ELETRONICA","EQUIPAJE","FERRETERIA","GRIFERIA","HERRAMIENTAS","ILUMINACION", |
|
"JARDIN","JOYERIA","JUGUETERIA","LACTEO","LAVANDERIA","LIBRO","LICOR","LIMPIEZA","MASCOTAS","MENAJE","MOBILIARIO","MUSICA","NIEVE","ORGANIZACION","OUTDOOR","PANADERIA", |
|
"PARRILLAS","PESCADERIA","PISCINAS","PISOSMUROS","PUERTASVENTANAS","RODADOS","SERVICIOS","TECNOLOGIA","TERRAZAS","UTILES","VESTUARIO","VINO","ZAPATERIA"] ), |
|
} |
|
) |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
path = dl_manager.download_and_extract(_DOWNLOAD_URL) |
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path, "is_test": False}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path, "is_test": True}), |
|
] |
|
|
|
def _generate_examples(self, filepath, is_test, test_size = 0.3): |
|
"""Generate Glosas Etiquetadas examples.""" |
|
with open('dataset2.csv', 'r', encoding='utf-8', errors='ignore') as csv_file: |
|
train_threshold = 40001 |
|
csv_reader = csv.reader( |
|
csv_file |
|
) |
|
|
|
for id_, row in enumerate(csv_reader): |
|
if id_ > 0: |
|
glosa, categoria = row |
|
current_row = id_, {"glosa": glosa, "categoria": int(categoria)} |
|
if (id_ < train_threshold) & (not is_test): |
|
yield current_row |
|
if (id_ >= train_threshold) & (is_test): |
|
yield current_row |
