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"""Loading script for the BLURB (Biomedical Language Understanding and Reasoning Benchmark) |
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benchmark for biomedical NLP.""" |
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import json |
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from pathlib import Path |
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import datasets |
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import shutil |
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_CITATION = """\ |
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@article{2022, |
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title={Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing}, |
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volume={3}, |
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ISSN={2637-8051}, |
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url={http://dx.doi.org/10.1145/3458754}, |
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DOI={10.1145/3458754}, |
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number={1}, |
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journal={ACM Transactions on Computing for Healthcare}, |
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publisher={Association for Computing Machinery (ACM)}, |
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author={Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung}, |
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year={2022}, |
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month={Jan}, |
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pages={1–23} |
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} |
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""" |
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_DESCRIPTION = """BLURB (Biomedical Language Understanding and Reasoning Benchmark.) |
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is a comprehensive benchmark for biomedical NLP, with 13 biomedical NLP datasets in 6 |
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tasks (NER, PICO, Relation Extraction, Sentence similarity, document classification, question answering). |
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Our aim is to facilitate investigations of biomedical natural language processing |
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with a specific focus on language model pretraining and to help accelerate progress in universal Biomedical |
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NLP applications. The table below compares the datasets comprising BLURB versus the various datasets used in |
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previous Biomedical and Clinical BERT language models.""" |
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_HOMEPAGE = "https://microsoft.github.io/BLURB/index.html" |
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_LICENSE = "TBD" |
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_VERSION = "1.0.0" |
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DATA_DIR = "blurb/" |
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logger = datasets.logging.get_logger(__name__) |
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CITATION_BC5_CHEM = """@article{article, |
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author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong}, |
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year = {2016}, |
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month = {05}, |
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pages = {baw068}, |
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title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction}, |
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volume = {2016}, |
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journal = {Database}, |
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doi = {10.1093/database/baw068} |
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} |
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""" |
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class BlurbConfig(datasets.BuilderConfig): |
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"""BuilderConfig for BLURB.""" |
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def __init__(self, task, data_url, citation, label_classes=("False", "True"), **kwargs): |
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"""BuilderConfig for BLURB. |
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Args: |
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task: `string` task the dataset is used for: 'ner', 'pico', 'rel-ext', 'sent-sim', 'doc-clas', 'qa' |
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features: `list[string]`, list of the features that will appear in the |
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feature dict. Should not include "label". |
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data_url: `string`, url to download the data files from. |
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citation: `string`, citation for the data set. |
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url: `string`, url for information about the data set. |
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label_classes: `list[string]`, the list of classes for the label if the |
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label is present as a string. Non-string labels will be cast to either |
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'False' or 'True'. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(BlurbConfig, self).__init__(version=datasets.Version(_VERSION), **kwargs) |
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self.task = task |
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self.label_classes = label_classes |
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self.data_url = data_url |
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self.citation = citation |
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if self.task == 'ner': |
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self.features = datasets.Features( |
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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(names=self.label_classes) |
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)} |
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) |
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self.base_url = f"{self.data_url}{self.name}/" |
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self.urls = { |
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"train": f"{self.base_url}{'train.tsv'}", |
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"validation": f"{self.base_url}{'devel.tsv'}", |
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"test": f"{self.base_url}{'test.tsv'}" |
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} |
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class Blurb(datasets.GeneratorBasedBuilder): |
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"""BLURB benchmark dataset for Biomedical Language Understanding and Reasoning Benchmark.""" |
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BUILDER_CONFIGS = [ |
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BlurbConfig(name='BC5CDR-chem-IOB', task='ner', label_classes=['O', 'B-Chemical', 'I-Chemical'], |
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data_url = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/", |
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description='BC5-CHEM', |
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citation=CITATION_BC5_CHEM) |
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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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"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-Chemical", |
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"I-Chemical", |
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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=_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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if self.config.task == 'ner': |
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downloaded_files = dl_manager.download_and_extract(self.config.urls) |
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return self._ner_split_generator(downloaded_files) |
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def _generate_examples(self, filepath): |
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print("Before the download") |
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logger.info("⏳ Generating examples from = %s", filepath) |
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if self.config.task == 'ner': |
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self._ner_example_generator(filepath) |
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def _ner_split_generator(self, downloaded_files): |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": downloaded_files["validation"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, |
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gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _ner_example_generator(self, filepath): |
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with open(filepath, encoding="utf-8") as f: |
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guid = 0 |
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tokens = [] |
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ner_tags = [] |
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for line in f: |
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if line == "" or line == "\n": |
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if tokens: |
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yield guid, { |
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"id": str(guid), |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
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guid += 1 |
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tokens = [] |
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ner_tags = [] |
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else: |
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splits = line.split("\t") |
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tokens.append(splits[0]) |
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ner_tags.append(splits[1].rstrip()) |
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yield guid, { |
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"id": str(guid), |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
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