Update labels.py

labels.py

@@ -50,64 +50,40 @@ _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",
-]
+_TOPICS = ["Society & Culture", "Science & Mathematics", "Health", "Education & Reference", "Computers & Internet", "Sports", "Business & Finance", "Entertainment & Music", "Family & Relationships", "Politics & Government"]
 
-
-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", ""),
-        ),
-    ]
+class AGNews(datasets.GeneratorBasedBuilder):
+    """AG News topic classification dataset."""
 
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "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"),
-                },
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["Society & Culture", "Science & Mathematics", "Health", "Education & Reference", "Computers & Internet", "Sports", "Business & Finance", "Entertainment & Music", "Family & Relationships", "Politics & Government"]),
+                }
             ),
-            supervised_keys=None
+            homepage="http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html",
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
         )
-        
+
     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 f:
-            rows = csv.reader(
-                f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+        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(rows):
+            for id_, row in enumerate(csv_reader):
                 label, title, description = row
                 # Original labels are [1, 2, 3, 4] ->
                 #                   ['World', 'Sports', 'Business', 'Sci/Tech']