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"""The Open WebText Corpus""" |
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import re |
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import datasets |
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from glob import glob |
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_CITATION = """\ |
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Dummy text |
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""" |
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_DESCRIPTION = """\ |
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An open-source replication of the WebText dataset from OpenAI. |
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""" |
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_N_DATA_FILES = 3 |
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_DATA_FILES = ["data/dummy-text-{:02d}.zip".format(i) for i in range(_N_DATA_FILES)] |
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print("_DATA_FILES", _DATA_FILES) |
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class Openwebtext(datasets.GeneratorBasedBuilder): |
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"""The Open WebText dataset.""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="plain_text", |
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description="Plain text", |
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version=datasets.Version("1.0.0"), |
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) |
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] |
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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({"text": datasets.Value("string")}), |
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homepage="", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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archives = dl_manager.download(_DATA_FILES) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ |
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"archive_iterators": [ |
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dl_manager.iter_archive(archive) for archive in archives |
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], |
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"iter_archive": dl_manager.iter_archive |
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}), |
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] |
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def _generate_examples(self, archive_iterators, iter_archive): |
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"""Yields examples.""" |
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for archive_iterator in archive_iterators: |
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for text_filepath, text_f in archive_iterator: |
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print(">>>>>", text_filepath) |
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idx = f"{text_filepath}" |
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print("id = ", id) |
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yield idx, {"text": re.sub("\n\n\n+", "\n\n", text_f.read().decode("utf-8")).strip()} |
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