Update labels.py

labels.py

@@ -18,9 +18,9 @@
 
 
 import csv
-import os
 
 import datasets
+from datasets.tasks import TextClassification
 
 
 _DESCRIPTION = """\
@@ -48,10 +48,20 @@ _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"
+_TRAIN_DOWNLOAD_URL = "https://github.com/edubrigham/data/blob/a43e2eb61d49a7c5f9b7856de2538dc1a7712e47/ag_news_csv/train.csv"
+_TEST_DOWNLOAD_URL = "https://github.com/edubrigham/data/blob/a43e2eb61d49a7c5f9b7856de2538dc1a7712e47/ag_news_csv/test.csv"
 
-_TOPICS = [
+
+class AGNews(datasets.GeneratorBasedBuilder):
+    """AG News topic classification dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=[
     "Society & Culture",
     "Science & Mathematics",
     "Health",
@@ -61,35 +71,12 @@ _TOPICS = [
     "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", ""),
-        ),
-    ]
-
-    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"),
-                },
+    "Politics & Government"]),
+                }
             ),
-            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):
@@ -101,14 +88,16 @@ class YahooAnswersTopics(datasets.GeneratorBasedBuilder):
         ]
 
     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],
-                }
-
+        """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}