Update glosas_etiquetadas.py

glosas_etiquetadas.py

@@ -1,47 +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
+import csv
+import datasets
+
+_DOWNLOAD_URL = "https://huggingface.co/datasets/cfigueroa/GE2/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