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"""20ng question 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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This data collection contains all the data used in our learning question classification experiments(see [1]), which has question class definitions, the training and testing question sets, examples of preprocessing the questions, feature definition scripts and examples of semantically related word features. |
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This work has been done by Xin Li and Dan Roth and supported by [2]. |
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""" |
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_CITATION = """""" |
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_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng/resolve/main/train.csv" |
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_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng/resolve/main/test.csv" |
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_VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng/raw/main/validaton.csv" |
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CATEGORY_MAPPING = {'comp.sys.mac.hardware': 0, |
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'comp.graphics': 1, |
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'sci.space': 2, |
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'talk.politics.guns': 3, |
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'sci.med': 4, |
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'comp.sys.ibm.pc.hardware': 5, |
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'comp.os.ms-windows.misc': 6, |
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'rec.motorcycles': 7, |
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'misc.forsale': 8, |
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'alt.atheism': 9, |
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'rec.autos': 10, |
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'sci.electronics': 11, |
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'comp.windows.x': 12, |
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'rec.sport.hockey': 13, |
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'rec.sport.baseball': 14, |
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'talk.politics.mideast': 15, |
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'sci.crypt': 16, |
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'soc.religion.christian': 17, |
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'talk.politics.misc': 18, |
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'talk.religion.misc': 19} |
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class NG(datasets.GeneratorBasedBuilder): |
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"""20ng 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=list(CATEGORY_MAPPING.keys())), |
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} |
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), |
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homepage="", |
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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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valid_path = dl_manager.download_and_extract(_VALID_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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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}), |
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] |
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def _generate_examples(self, filepath): |
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"""Generate 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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text, label = row |
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label = CATEGORY_MAPPING[label] |
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yield id_, {"text": text, "label": label} |
