Update labels.py

labels.py

@@ -51,44 +51,68 @@ _CITATION = """\
 _TRAIN_DOWNLOAD_URL = "https://github.com/edubrigham/data/blob/541db4cdbb566aa5909e3eb4904d64f9683e5d4a/yahoo_answers_csv/train.csv"
 _TEST_DOWNLOAD_URL = "https://github.com/edubrigham/data/blob/541db4cdbb566aa5909e3eb4904d64f9683e5d4a/yahoo_answers_csv/test.csv"
 
+_TOPICS = [
+    "Society & Culture",
+    "Science & Mathematics",
+    "Health",
+    "Education & Reference",
+    "Computers & Internet",
+    "Sports",
+    "Business & Finance",
+    "Entertainment & Music",
+    "Family & Relationships",
+    "Politics & Government",
+]
 
-class AGNews(datasets.GeneratorBasedBuilder):
-    """AG News topic classification dataset."""
+
+class YahooAnswersTopics(datasets.GeneratorBasedBuilder):
+    "Yahoo! Answers Topic Classification Dataset"
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="yahoo_answers_topics",
+            version=datasets.Version("1.0.0", ""),
+        ),
+    ]
 
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "text": datasets.Value("string"),
-                    "labels": datasets.features.ClassLabel(names=["Society & Culture", "Science & Mathematics", "Health","Education & Reference", "Computers & Internet", "Sports", "Business & Finance", "Entertainment & Music", "Family & Relationships", "Politics & Government"]),
-                }
+                    "id": datasets.Value("int32"),
+                    "topic": datasets.features.ClassLabel(names=_TOPICS),
+                    "question_title": datasets.Value("string"),
+                    "question_content": datasets.Value("string"),
+                    "best_answer": datasets.Value("string"),
+                },
             ),
-            homepage="http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="labels")],
+            supervised_keys=None,
+            homepage="https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset",
         )
+        
 
     def _split_generators(self, dl_manager):
         train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
         test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
         ]
-
-    def _generate_examples(self, filepath):
-        """Generate AG News examples."""
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(
-                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
-            )
-            for id_, row in enumerate(csv_reader):
-                label, title, description = row
-                # Original labels are [1, 2, 3, 4] ->
-                #                   ['World', 'Sports', 'Business', 'Sci/Tech']
-                # Re-map to [0, 1, 2, 3].
-                label = int(label) - 1
-                text = " ".join((title, description))
-                yield id_, {"text": text, "labels": label}
+            
+     def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            rows = csv.reader(f)
+            for i, row in enumerate(rows):
+                yield i, {
+                    "id": i,
+                    "topic": int(row[0]) - 1,
+                    "question_title": row[1],
+                    "question_content": row[2],
+                    "best_answer": row[3],
+                }