Upload GE2.py

GE2.py

@@ -0,0 +1,47 @@
+import csv
+import datasets
+
+_DOWNLOAD_URL = "https://huggingface.co/datasets/cfigueroa/glosas_etiquetadas/resolve/main/datasetNew.csv"
+
+class GlosasEtiquetadas(datasets.GeneratorBasedBuilder):
+    """Glosas Etiquetadas classification dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names = ["LIBRERIA_ACCESORIOS","ALIMENTOS","ASEOPERSONAL","VEHICULOS",
+"BANO","BEBE","BEBIDAS","CARNE","CERVEZA","CONSTRUCCION",
+"DECORACION","DEPORTE","DORMITORIO","ELECTRONICA","EQUIPAJE","FERRETERIA",
+"GRIFERIA","ILUMINACION","JARDIN","JOYERIA","JUGUETERIA","LACTEO","LAVANDERIA",
+"LICORES","LIMPIEZA","MASCOTAS","MENAJE","MOBILIARIO",
+"ORGANIZACION","OUTDOOR","PANADERIA","PISOSMUROS","PUERTASVENTANAS",
+"SERVICIOS","TECNOLOGIA","TERRAZAS","VESTUARIO","VINOS","ZAPATERIA", "ILEGIBLE"] ),
+                }
+            )
+        )
+
+    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, 'r', encoding='latin-1')  as csv_file:
+            train_threshold = 40001
+            csv_reader = csv.reader(
+                csv_file, delimiter=';'
+            )
+            # next(csv_reader, None)  # skip the headers
+            for id_, row in enumerate(csv_reader):
+                if id_ > 0:
+                    text, label = row
+                    current_row = id_, {"text": text, "label": int(label)}
+                    if (id_ < train_threshold) & (not is_test):
+                        yield current_row
+                    if (id_ >= train_threshold) & (is_test):
+                        yield current_row