Update labels.py

labels.py

@@ -15,76 +15,51 @@
 """Yahoo! Answers Topic Classification Dataset"""
 
 
-import csv
-import os
+_TRAIN_DOWNLOAD_URL = "https://drive.google.com/file/d/1Ehv1SSZ4n7ZLpUp7aSKNwHuC8UOgdfzL/view?usp=sharing"
+_TEST_DOWNLOAD_URL = "https://drive.google.com/file/d/1UWUuTEkK20Pz-H0rt78n91hHeVUhtCh1/view?usp=sharing"
 
-import datasets
 
-
-_DESCRIPTION = """
-Yahoo! Answers Topic Classification is text classification dataset. \
-The dataset is the Yahoo! Answers corpus as of 10/25/2007. \
-The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. \
-From all the answers and other meta-information, this dataset only used the best answer content and the main category information.
-"""
-
-_URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU"
-
-_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=["World", "Sports", "Business", "Sci/Tech"]),
+                }
             ),
-            supervised_keys=None,
-            homepage="https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset",
+            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):
-        data_dir = dl_manager.download_and_extract(_URL)
-
-        # Extracting (un-taring) the training data
-        data_dir = os.path.join(data_dir, "yahoo_answers_csv")
+        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": os.path.join(data_dir, "train.csv")}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")}
-            ),
+            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, "label": label}
+
+
     def _generate_examples(self, filepath):
         with open(filepath, encoding="utf-8") as f:
             rows = csv.reader(f)