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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{mutinda2022pico, |
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title = {PICO Corpus: A Publicly Available Corpus to Support Automatic Data Extraction from Biomedical Literature}, |
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author = {Mutinda, Faith and Liew, Kongmeng and Yada, Shuntaro and Wakamiya, Shoko and Aramaki, Eiji}, |
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booktitle = {Proceedings of the first Workshop on Information Extraction from Scientific Publications}, |
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pages = {26--31}, |
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year = {2022} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The corpus consists of about 1,011 PubMed abstracts which are RCTs related |
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to breast cancer. For each abstract, text snippets that identify the |
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Participants, Intervention, Control, and Outcome (PICO elements) are annotated. |
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The abstracts were annotated using BRAT (https://brat.nlplab.org/) and later |
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converted to IOB format. |
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""" |
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_URL = "https://raw.githubusercontent.com/Martin-Masson/pico-breast-cancer/main/pico_iob/" |
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_TRAINING_FILE = "train.txt" |
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_DEV_FILE = "dev.txt" |
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_TEST_FILE = "test.txt" |
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_ALL_FILE = "all.txt" |
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_SHORT_FILE = "all_short.txt" |
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class PicoBreastCancerConfig(datasets.BuilderConfig): |
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"""BuilderConfig for PicoBreastCancer""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for PicoBreastCancer. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(PicoBreastCancerConfig, self).__init__(**kwargs) |
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class PicoBreastCancer(datasets.GeneratorBasedBuilder): |
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"""A corpus of about 1,011 PubMed abstracts from RCTs related to breast cancer.""" |
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BUILDER_CONFIGS = [ |
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PicoBreastCancerConfig(name="split", version=datasets.Version("1.0.0"), description="Train/Validation/Test splits containing respectively 80%/10%/10% of all the abstracts."), |
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PicoBreastCancerConfig(name="all", version=datasets.Version("1.0.0"), description="Single dataset containing all the absracts for custom splitting."), |
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PicoBreastCancerConfig(name="short", version=datasets.Version("1.0.0"), description="Similar to 'all' but with shortened examples to account for embeddings size."), |
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] |
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DEFAULT_CONFIG_NAME = "split" |
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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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"id": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"O", |
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"B-total-participants", |
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"I-total-participants", |
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"B-intervention-participants", |
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"I-intervention-participants", |
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"B-control-participants", |
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"I-control-participants", |
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"B-age", |
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"I-age", |
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"B-eligibility", |
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"I-eligibility", |
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"B-ethinicity", |
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"I-ethinicity", |
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"B-condition", |
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"I-condition", |
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"B-location", |
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"I-location", |
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"B-intervention", |
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"I-intervention", |
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"B-control", |
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"I-control", |
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"B-outcome", |
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"I-outcome", |
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"B-outcome-measure", |
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"I-outcome-measure", |
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"B-iv-bin-abs", |
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"I-iv-bin-abs", |
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"B-cv-bin-abs", |
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"I-cv-bin-abs", |
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"B-iv-bin-percent", |
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"I-iv-bin-percent", |
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"B-cv-bin-percent", |
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"I-cv-bin-percent", |
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"B-iv-cont-mean", |
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"I-iv-cont-mean", |
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"B-cv-cont-mean", |
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"I-cv-cont-mean", |
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"B-iv-cont-median", |
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"I-iv-cont-median", |
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"B-cv-cont-median", |
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"I-cv-cont-median", |
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"B-iv-cont-sd", |
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"I-iv-cont-sd", |
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"B-cv-cont-sd", |
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"I-cv-cont-sd", |
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"B-iv-cont-q1", |
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"I-iv-cont-q1", |
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"B-cv-cont-q1", |
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"I-cv-cont-q1", |
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"B-iv-cont-q3", |
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"I-iv-cont-q3", |
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"B-cv-cont-q3", |
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"I-cv-cont-q3", |
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] |
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) |
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), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/Martin-Masson/pico-corpus", |
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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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urls_to_download = { |
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"train": f"{_URL}{_TRAINING_FILE}", |
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"dev": f"{_URL}{_DEV_FILE}", |
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"test": f"{_URL}{_TEST_FILE}", |
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"all": f"{_URL}{_ALL_FILE}", |
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"short": f"{_URL}{_SHORT_FILE}", |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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if self.config.name == "split": |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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else: |
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file = "all" if self.config.name == "all" else "short" |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files[file]}), |
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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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idx = 0 |
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tokens = [] |
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ner_tags = [] |
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lines = f.read().splitlines() |
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for line in lines: |
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if not line: |
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yield idx, { |
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"id": str(idx), |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
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idx += 1 |
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tokens = [] |
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ner_tags = [] |
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else: |
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splits = line.rstrip().rsplit(" ", 1) |
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tokens.append(splits[0]) |
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ner_tags.append(splits[1]) |
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if tokens: |
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yield idx, { |
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"id": str(idx), |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
