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import csv |
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import datasets |
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_DOWNLOAD_URL = "https://huggingface.co/datasets/mrojas/task1a/resolve/main/data.csv" |
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class Task1a(datasets.GeneratorBasedBuilder): |
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"""Task1a classification dataset.""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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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.ClassLabel(names = ["0", "1"]), |
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} |
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) |
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) |
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def _split_generators(self, dl_manager): |
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path = dl_manager.download_and_extract(_DOWNLOAD_URL) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path, "is_test": False}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path, "is_test": True}), |
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] |
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def _generate_examples(self, filepath, is_test, test_size = 0.3): |
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"""Generate examples.""" |
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with open(filepath, encoding="utf-8") as csv_file: |
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train_threshold = 122 |
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csv_reader = csv.reader( |
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csv_file |
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) |
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for id_, row in enumerate(csv_reader): |
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if id_ > 0: |
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print(row) |
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text, label = row |
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current_row = id_, {"text": text, "label": int(label)} |
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if (id_ < train_threshold) & (not is_test): |
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yield current_row |
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if (id_ >= train_threshold) & (is_test): |
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yield current_row |
