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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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_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/linxinyuan/cola/resolve/main/train.csv" |
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_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/linxinyuan/cola/resolve/main/test.csv" |
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class mind(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="cola", |
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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=['0', '1']), |
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} |
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), |
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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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with open(filepath, encoding="utf-8") as csv_file: |
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csv_reader = csv.reader(csv_file, delimiter="\t") |
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for id_, row in enumerate(csv_reader): |
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yield id_, {"text": row[3], "label": (int)(row[1])} |
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