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"""The Stories CC dataset.""" |
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import datasets |
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_DESCRIPTION = """\ |
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CC-Stories (or STORIES) is a dataset for common sense reasoning and language modeling. It was constructed by aggregating documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions in commonsense reasoning tasks. The top 1.0% of highest ranked documents is chosen as the new training corpus. |
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""" |
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_CITATION = """\ |
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@article{Trinh2018ASM, |
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title={A Simple Method for Commonsense Reasoning}, |
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author={Trieu H. Trinh and Quoc V. Le}, |
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journal={ArXiv}, |
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year={2018}, |
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volume={abs/1806.02847} |
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} |
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""" |
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URL = "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories.txt.gz" |
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_DATASET_URLS = { |
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'train': "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories.txt", |
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} |
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class CCStoriesConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CC Stories.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for CC Stories |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(CCStoriesConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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class Bookcorpus(datasets.GeneratorBasedBuilder): |
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"""CC Stories dataset.""" |
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BUILDER_CONFIGS = [ |
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CCStoriesConfig( |
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name="plain_text", |
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description="Plain text", |
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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( |
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{ |
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"text": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage="", |
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citation=_CITATION, |
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) |
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def _vocab_text_gen(self, archive): |
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for _, ex in self._generate_examples(archive): |
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yield ex["text"] |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_DATASET_URLS) |
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splits = [ |
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datasets.SplitGenerator( |
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name=split, |
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gen_kwargs={ |
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"files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split], |
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}, |
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) for split in downloaded_files |
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] |
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return splits |
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def _generate_examples(self, files): |
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_id = 0 |
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for path, file in files: |
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for line in file: |
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yield _id, {"text": line.decode("utf-8").strip()} |
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_id += 1 |
