Update Tc.py

Tc.py

@@ -1,52 +1,82 @@
 import csv
+
 import datasets
 from datasets.tasks import TextClassification
 
 
-_DESCRIPTION = """\
-IUT Ticket Classification
-"""
+logger = datasets.logging.get_logger(__name__)
 
-_DOWNLOAD_URLS = {
 
+_CITATION = """Citation"""
+_DESCRIPTION = """Description"""
+
+_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 SentimentDKSF(datasets.GeneratorBasedBuilder):
-    """IUT Tickets"""
+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,
+        ),
+    ]
 
     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": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["drop_withdraw", "centralauthentication_email","supervisor_seminar_proposal_defense", "registration"])}
+                {text_column: datasets.Value("string"), label_column: datasets.features.ClassLabel(names=label_names)}
             ),
-            supervised_keys=None,
             homepage="https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification",
-            task_templates=[TextClassification(text_column="text", label_column="label")],
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column=text_column, label_column=label_column)],
         )
 
     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):
-        """Generate examples."""
+        """
+        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"})
+        ```
+        """
         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
-            )
-            # skip the first row if your csv file has a header row
+            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
             next(csv_reader, None)
+
             for id_, row in enumerate(csv_reader):
-                text, label = row
+                text, label  = row
                 label = label2id(label)
-                yield id_, {"text": text, "label": label}
-                yield id_, {"text": text, "label": label}
+                # Optional preprocessing here
+                yield id_, {"text": text, "label": label}