Update depression-reddit-cleaned.py

depression-reddit-cleaned.py

@@ -15,9 +15,9 @@ from datasets.tasks import TextClassification
 
 _DESCRIPTION = """\
 The dataset provided is a Depression: Reddit Dataset (Cleaned)containing approximately
-7,000 labeled instances. It consists of two main features: 'clean_text' and 'is_depression'.
-The 'clean_text' feature contains the text data from Reddit posts related to depression, while
-the 'is_depression' feature indicates whether a post is classified as depression or not.
+7,000 labeled instances. It consists of two main features: 'text' and 'label'.
+The 'text' feature contains the text data from Reddit posts related to depression, while
+the 'label' feature indicates whether a post is classified as depression or not.
 
 The raw data for this dataset was collected by web scraping Subreddits. To ensure the data's
 quality and usefulness, multiple natural language processing (NLP) techniques were applied
@@ -46,14 +46,14 @@ class DepressionRedditCleaned(datasets.GeneratorBasedBuilder):
 			description=_DESCRIPTION,
 			features=datasets.Features(
 				{
-					"clean_text": datasets.Value("string"),
-					"is_depression": datasets.features.ClassLabel(
+					"text": datasets.Value("string"),
+					"label": datasets.features.ClassLabel(
                         num_classes=2,
 						names=["not_depression", "depression"]
 					)
 				}
 			),
-            task_templates=[TextClassification(text_column="clean_text", label_column="is_depression")]
+            task_templates=[TextClassification(text_column="text", label_column="label")]
 		)
 
 	def _split_generators(self, dl_manager):
@@ -72,5 +72,5 @@ class DepressionRedditCleaned(datasets.GeneratorBasedBuilder):
 			# call next to skip header
 			next(csv_reader)
 			for id_, row in enumerate(csv_reader):
-				clean_text, is_depression = row
-				yield id_, {"clean_text": clean_text, "is_depression": is_depression}
+				text, label = row
+				yield id_, {"text": text, "label": label}