Add test set

ag_news_pt.py

@@ -0,0 +1,94 @@
+# coding=utf-8
+# 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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+# Lint as: python3
+"""AG News topic classification dataset."""
+
+
+import csv
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_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
+([email protected]) 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).
+"""
+
+_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://huggingface.co/datasets/hugo/ag_news_pt/raw/main/train.csv"
+_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/hugo/ag_news_pt/raw/main/test.csv"
+
+
+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=["Mundo", "Esportes", "Negócios", "Tecnologia"]),
+                }
+            ),
+            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}),
+        ]
+
+    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}