Update labels.py

labels.py

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+ag_news.py
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+Update files from the datasets library (from 1.8.0)
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 # coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors.
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,87 +61,83 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Yahoo! Answers Topic Classification Dataset"""
+
+# Lint as: python3
+"""AG News topic classification dataset."""
 
 
 import csv
-import os
 
 import datasets
+from datasets.tasks import TextClassification
 
 
-_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.
+_DESCRIPTION = """\
+AG is a collection of more than 1 million news articles. News articles have been
+gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
+activity. ComeToMyHead is an academic news search engine which has been running
+since July, 2004. The dataset is provided by the academic comunity for research
+purposes in data mining (clustering, classification, etc), information retrieval
+(ranking, search, etc), xml, data compression, data streaming, and any other
+non-commercial activity. For more information, please refer to the link
+http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
+The AG's news topic classification dataset is constructed by Xiang Zhang
+(xiang.zhang@nyu.edu) from the dataset above. It is used as a text
+classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann
+LeCun. Character-level Convolutional Networks for Text Classification. Advances
+in Neural Information Processing Systems 28 (NIPS 2015).
 """
 
-_URL = "https://drive.google.com/uc?export=download&id=19SSlf8ohOAxWVda-cFFPZA1e_FZ4TJl9"
-
-_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", ""),
-        ),
-    ]
+_CITATION = """\
+@inproceedings{Zhang2015CharacterlevelCN,
+  title={Character-level Convolutional Networks for Text Classification},
+  author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
+  booktitle={NIPS},
+  year={2015}
+}
+"""
+
+_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"
+
+
+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="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):
-        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}
+