Update tc.py

tc.py

@@ -1,82 +1,51 @@
 import csv
-
 import datasets
 from datasets.tasks import TextClassification
 
 
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """Citation"""
-_DESCRIPTION = """Tickets of IUT university"""
+_DESCRIPTION = """\
+IUT Ticket Classification
+"""
 
 _DOWNLOAD_URLS = {
+
     "train": "https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification/raw/main/tc_train.csv",
-    "test": "https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification/raw/main/tc_test.csv",
+    "test": "https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification/raw/main/tc_test.csv"
 }
 
 
-class DatasetNameConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(DatasetNameConfig, self).__init__(**kwargs)
-
-
-class DatasetName(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        DatasetNameConfig(
-            name="University's Tickets",
-            version=datasets.Version("1.1.1"),
-            description=_DESCRIPTION,
-        ),
-    ]
+class SentimentDKSF(datasets.GeneratorBasedBuilder):
+    """IUT Tickets"""
 
     def _info(self):
-        text_column = "text"
-        label_column = "label"
-        # TODO PROVIDE THE LABELS HERE
-        label_names = ['drop_withdraw', 'centralauthentication_email', 'supervisor_seminar_proposal_defense', 'registration']
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(
-                {text_column: datasets.Value("string"), label_column: datasets.features.ClassLabel(names=label_names)}
+                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=['drop_withdraw', 'centralauthentication_email', 'supervisor_seminar_proposal_defense', 'registration'])}
             ),
+            supervised_keys=None,
             homepage="https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column=text_column, label_column=label_column)],
+            task_templates=[TextClassification(text_column="text", label_column="label")],
         )
 
     def _split_generators(self, dl_manager):
-        """
-        Return SplitGenerators.
-        """
         train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
         test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
-
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
             datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
         ]
 
-    # TODO
     def _generate_examples(self, filepath):
-        """
-        Per each file_path read the csv file and iterate it.
-        For each row yield a tuple of (id, {"text": ..., "label": ..., ...})
-        Each call to this method yields an output like below:
-        ```
-        (123, {"text": "I liked it", "label": "positive"})
-        ```
-        """
+        """Generate examples."""
         label2id = self.info.features[self.info.task_templates[0].label_column].str2int
-        logger.info("⏳ Generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True)
-
-            # Uncomment below line to skip the first row if your csv file has a header row
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', skipinitialspace=True
+            )
+            # skip the first row if your csv file has a header row
             next(csv_reader, None)
-
             for id_, row in enumerate(csv_reader):
                 label, text = row
                 label = label2id(label)
-                # Optional preprocessing here
-                yield id_, {"text": text, "label": label}
+                yield id_, {"text": text, "label": label}