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import os |
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import re |
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from pathlib import Path |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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TXT_PATTERN = r"^.*\.txt$" |
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_CITATION = """\ |
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@misc{https://doi.org/10.48550/arxiv.2210.06345, |
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doi = {10.48550/ARXIV.2210.06345}, |
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url = {https://arxiv.org/abs/2210.06345}, |
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author = {Liévin, Valentin and Motzfeldt, Andreas Geert and Jensen, Ida Riis and Winther, Ole}, |
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keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; H.3.3; I.2.1}, |
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title = {Variational Open-Domain Question Answering}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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""" |
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_VERSION = "0.0.1" |
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_HOMEPAGE = "https://github.com/VodLM" |
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_DESCRIPTION = """\ |
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The MedWiki corpus, which is distributed under the MIT license, |
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consists of a subset of 4.5% of the English Wikipedia articles |
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and has been specifically curated for the MedMCQA and USMLE datasets. |
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The collection was created by utilizing the Wikipedia API to search |
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for articles related to each answer option present in the MedMCQA and |
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USMLE datasets. The top ten Wikipedia articles for each answer option |
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were selected and included in the final corpus. This subset covers a |
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wide range of topics in the field of medicine that could be relevant |
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for answering questions in this domain. |
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questions. |
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""" |
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_URL = "https://f001.backblazeb2.com/file/FindZebraData/fz-openqa/datasets/medwiki_v6.zip" |
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class MedWikipediaCorpusConfig(datasets.BuilderConfig): |
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"""BuilderConfig for the MedQa English Corpus objecxt.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for the Corpus object. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(MedWikipediaCorpusConfig, self).__init__(**kwargs) |
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class MedWikipediaCorpusGenerator(datasets.GeneratorBasedBuilder): |
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"""MedWikipediaCorpus Dataset. Version 0.0.1""" |
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BUILDER_CONFIGS = [ |
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MedWikipediaCorpusConfig( |
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name="plain_text", |
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version=datasets.Version(_VERSION, ""), |
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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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"document.idx": datasets.Value("int32"), |
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"document.text": datasets.Value("string"), |
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"document.title": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_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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"""Returns SplitGenerators.""" |
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downloaded_file = dl_manager.download_and_extract(_URL) |
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if not Path(downloaded_file).is_dir(): |
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raise Exception( |
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f"Could not download the dataset Content of `downloaded_file`:" |
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f"{open(downloaded_file, 'r').read()}" |
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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={"output_dir": Path(downloaded_file)}, |
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) |
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] |
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def _generate_examples(self, output_dir: str): |
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logger.info("generating examples from = %s", output_dir) |
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paths = [ |
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p |
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for p in Path(output_dir).iterdir() |
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if p.is_dir() and (p.name.startswith("med_x_wiki") or p.name.startswith("wikipedia")) |
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] |
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assert len(paths) == 1, f"Found {len(paths)} directories in {output_dir}: {paths}" |
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path = paths[0] |
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data_files = [os.path.join(path, p) for p in os.listdir(path) if re.findall(TXT_PATTERN, p)] |
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for i, fn in enumerate(data_files): |
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with open(fn, "r") as f: |
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title = f.readline() |
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text = f.read() |
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yield i, {"document.text": text, "document.idx": i, "document.title": title} |
