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import json |
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import datasets |
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from datasets.tasks import QuestionAnsweringExtractive |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@article{2016arXiv160605250R, |
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author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, |
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Konstantin and {Liang}, Percy}, |
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title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", |
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journal = {arXiv e-prints}, |
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year = 2016, |
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eid = {arXiv:1606.05250}, |
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pages = {arXiv:1606.05250}, |
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archivePrefix = {arXiv}, |
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eprint = {1606.05250}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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Demo... |
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""" |
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_URL = "https://github.com/rajivmehtaflex/PythonExp/raw/main/HFDatasets/reddit_data.tar.gz" |
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class RedditTopicsTargz(datasets.GeneratorBasedBuilder): |
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"""SQUAD: The Stanford Question Answering Dataset. Version 1.1.""" |
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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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"sub": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"selftext": datasets.Value("string"), |
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"upvote_ratio": datasets.Value("float32"), |
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"id": datasets.Value("string"), |
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"created_utc": datasets.Value("float32"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/rajivmehtapy/", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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path = dl_manager.download_and_extract(_URL) |
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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": path+'/reddit_data.jsonl'} |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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idx = 0 |
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with open(filepath, encoding="utf-8") as fp: |
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for line in fp: |
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obj = json.loads(line) |
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yield idx, obj |
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idx += 1 |
