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"""ELI5-Category: A categorized open-domain QA dataset.""" |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{eli5-category, |
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author = {Jingsong Gao and |
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Qingren Zhou and |
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Rui Qiu}, |
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title = {{ELI5-Category:} A categorized open-domain QA dataset}, |
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year = {2021} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The ELI5-Category dataset is a smaller but newer and categorized version of the original ELI5 dataset. \ |
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After 2017, a tagging system was introduced to this subreddit so that the questions can be categorized \ |
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into different topics according to their tags. Since the training and validation set is built by questions \ |
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in different topics, the dataset is expected to alleviate the train/validation overlapping issue \ |
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in the original ELI5 dataset. |
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""" |
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class ELI5CategoryConfig(datasets.BuilderConfig): |
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"""BuilderConfig for ELI5Category.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for ELI5Category. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(ELI5CategoryConfig, self).__init__(**kwargs) |
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class ELI5Category(datasets.GeneratorBasedBuilder): |
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"""ELI5-Category: A categorized open-domain QA dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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ELI5CategoryConfig( |
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name="default", |
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version=datasets.Version("1.0.0"), |
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description="Default config", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "default" |
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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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"q_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"selftext": datasets.Value("string"), |
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"category": datasets.Value("string"), |
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"subreddit": datasets.Value("string"), |
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"answers": { |
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"a_id": datasets.features.Sequence(datasets.Value("string")), |
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"text": datasets.features.Sequence(datasets.Value("string")), |
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"score": datasets.features.Sequence(datasets.Value("int32")), |
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"text_urls": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))), |
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}, |
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"title_urls": datasets.features.Sequence(datasets.Value("string")), |
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"selftext_urls": datasets.features.Sequence(datasets.Value("string")), |
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} |
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), |
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supervised_keys=None, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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_URL = "https://jingshensn2.github.io/eli5c/datasets/" |
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downloaded_files = dl_manager.download_and_extract( |
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{ |
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"train": _URL + "eli5-category-train.json.gz", |
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"val1": _URL + "eli5-category-validation-1.json.gz", |
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"val2": _URL + "eli5-category-validation-2.json.gz", |
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"test": _URL + "eli5-category-test.json.gz", |
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} |
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) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": downloaded_files["train"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("validation1"), |
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gen_kwargs={"filepath": downloaded_files["val1"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("validation2"), |
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gen_kwargs={"filepath": downloaded_files["val2"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": downloaded_files["test"]}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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example = json.load(f) |
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for id_, row in enumerate(example): |
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yield id_, row |
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