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glosas_etiquetadas.py

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+import csv
+import datasets
+
+_DOWNLOAD_URL = "https://huggingface.co/datasets/cfigueroa/glosas_etiquetadas/blob/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(filepath, encoding="utf-8") as csv_file:
+            train_threshold = 70001
+            csv_reader = csv.reader(
+                csv_file
+            )
+            # next(csv_reader, None)  # skip the headers
+            for id_, row in enumerate(csv_reader):
+                if id_ > 0:
+                    text, label = 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