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"""AG News topic classification dataset.""" |
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import csv |
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import datasets |
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from datasets.tasks import TextClassification |
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_DESCRIPTION = """\ |
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AG is a collection of more than 1 million news articles. News articles have been |
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gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of |
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activity. ComeToMyHead is an academic news search engine which has been running |
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since July, 2004. The dataset is provided by the academic comunity for research |
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purposes in data mining (clustering, classification, etc), information retrieval |
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(ranking, search, etc), xml, data compression, data streaming, and any other |
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non-commercial activity. For more information, please refer to the link |
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http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html . |
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The AG's news topic classification dataset is constructed by Xiang Zhang |
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(xiang.zhang@nyu.edu) from the dataset above. It is used as a text |
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classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann |
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LeCun. Character-level Convolutional Networks for Text Classification. Advances |
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in Neural Information Processing Systems 28 (NIPS 2015). |
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""" |
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_CITATION = """\ |
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@inproceedings{Zhang2015CharacterlevelCN, |
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title={Character-level Convolutional Networks for Text Classification}, |
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author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun}, |
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booktitle={NIPS}, |
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year={2015} |
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} |
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""" |
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_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/train.csv" |
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_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/test.csv" |
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class AGNews(datasets.GeneratorBasedBuilder): |
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"""AG News topic classification dataset.""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"label": datasets.features.ClassLabel(names=["World", "Sports", "Business", "Sci/Tech"]), |
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} |
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), |
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homepage="http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html", |
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citation=_CITATION, |
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task_templates=[TextClassification(text_column="text", label_column="label")], |
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) |
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def _split_generators(self, dl_manager): |
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train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) |
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test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), |
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] |
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def _generate_examples(self, filepath): |
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"""Generate AG News examples.""" |
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with open(filepath, encoding="utf-8") as csv_file: |
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csv_reader = csv.reader( |
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csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True |
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) |
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for id_, row in enumerate(csv_reader): |
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label, title, description = row |
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label = int(label) - 1 |
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text = " ".join((title, description)) |
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yield id_, {"text": text, "label": label} |
