Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

ag_news.py

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+# 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."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_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://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/train.csv"
+_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/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=["World", "Sports", "Business", "Sci/Tech"]),
+                }
+            ),
+            homepage="http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html",
+            citation=_CITATION,
+        )
+
+    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}

dataset_infos.json

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+{"default": {"description": "AG is a collection of more than 1 million news articles. News articles have been \ngathered from more than 2000 news sources by ComeToMyHead in more than 1 year of \nactivity. ComeToMyHead is an academic news search engine which has been running \nsince July, 2004. The dataset is provided by the academic comunity for research \npurposes in data mining (clustering, classification, etc), information retrieval \n(ranking, search, etc), xml, data compression, data streaming, and any other \nnon-commercial activity. For more information, please refer to the link \nhttp://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .\n\nThe AG's news topic classification dataset is constructed by Xiang Zhang \n([email protected]) from the dataset above. It is used as a text \nclassification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann \nLeCun. Character-level Convolutional Networks for Text Classification. Advances \nin Neural Information Processing Systems 28 (NIPS 2015).\n", "citation": "@inproceedings{Zhang2015CharacterlevelCN,\n  title={Character-level Convolutional Networks for Text Classification},\n  author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},\n  booktitle={NIPS},\n  year={2015}\n}\n", "homepage": "http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 4, "names": ["World", "Sports", "Business", "Sci/Tech"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "supervised_keys": null, "builder_name": "ag_news", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 29817351, "num_examples": 120000, "dataset_name": "ag_news"}, "test": {"name": "test", "num_bytes": 1879478, "num_examples": 7600, "dataset_name": "ag_news"}}, "download_checksums": {"https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/train.csv": {"num_bytes": 29470338, "checksum": "76a0a2d2f92b286371fe4d4044640910a04a803fdd2538e0f3f29a5c6f6b672e"}, "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/test.csv": {"num_bytes": 1857427, "checksum": "521465c2428ed7f02f8d6db6ffdd4b5447c1c701962353eb2c40d548c3c85699"}}, "download_size": 31327765, "dataset_size": 31696829, "size_in_bytes": 63024594}}

dummy/0.0.0/dummy_data.zip

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+size 473