"""Loading script for the BLURB (Biomedical Language Understanding and Reasoning Benchmark) benchmark for biomedical NLP.""" import json from pathlib import Path import datasets import shutil import pandas as pd from constants import CITATIONS, DESCRIPTIONS, HOMEPAGES, DATA_URL _LICENSE = "TBD" _VERSION = "1.0.0" DATA_DIR = "blurb/" logger = datasets.logging.get_logger(__name__) class BlurbConfig(datasets.BuilderConfig): """BuilderConfig for BLURB.""" def __init__(self, task, data_url, citation, homepage, label_classes=None, **kwargs): """BuilderConfig for BLURB. Args: task: `string` task the dataset is used for: 'ner', 'pico', 'rel-ext', 'sent-sim', 'doc-clas', 'qa' features: `list[string]`, list of the features that will appear in the feature dict. Should not include "label". data_url: `string`, url to download the data files from. citation: `string`, citation for the data set. url: `string`, url for information about the data set. label_classes: `list[string]`, the list of classes for the label if the label is present as a string. Non-string labels will be cast to either 'False' or 'True'. **kwargs: keyword arguments forwarded to super. """ # Version history: super(BlurbConfig, self).__init__(version=datasets.Version(_VERSION), **kwargs) self.task = task self.label_classes = label_classes self.data_url = data_url self.citation = citation self.homepage = homepage if self.task == 'ner': self.features = datasets.Features( {"id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel(names=self.label_classes) )} ) self.base_url = f"{self.data_url}{self.name}/" self.urls = { "train": f"{self.base_url}{'train.tsv'}", "validation": f"{self.base_url}{'devel.tsv'}", "test": f"{self.base_url}{'test.tsv'}" } if self.task == 'sent-sim': self.features = datasets.Features( { "sentence1": datasets.Value("string"), "sentence2": datasets.Value("string"), "score": datasets.Value("float32"), } ) class Blurb(datasets.GeneratorBasedBuilder): """BLURB benchmark dataset for Biomedical Language Understanding and Reasoning Benchmark.""" BUILDER_CONFIGS = [ BlurbConfig(name='BC5CDR-chem-IOB', task='ner', label_classes=['O', 'B-Chemical', 'I-Chemical'], data_url = DATA_URL['BC5CDR-chem-IOB'], description=DESCRIPTIONS['BC5CDR-chem-IOB'], citation=CITATIONS['BC5CDR-chem-IOB'], homepage=HOMEPAGES['BC5CDR-chem-IOB']), BlurbConfig(name='BC5CDR-disease-IOB', task='ner', label_classes=['O', 'B-Disease', 'I-Disease'], data_url = DATA_URL['BC5CDR-disease-IOB'], description=DESCRIPTIONS['BC5CDR-disease-IOB'], citation=CITATIONS['BC5CDR-disease-IOB'], homepage=HOMEPAGES['BC5CDR-disease-IOB']), BlurbConfig(name='BC2GM-IOB', task='ner', label_classes=['O', 'B-GENE', 'I-GENE'], data_url = DATA_URL['BC2GM-IOB'], description=DESCRIPTIONS['BC2GM-IOB'], citation=CITATIONS['BC2GM-IOB'], homepage=HOMEPAGES['BC2GM-IOB']), BlurbConfig(name='NCBI-disease-IOB', task='ner', label_classes=['O', 'B-Disease', 'I-Disease'], data_url = DATA_URL['NCBI-disease-IOB'], description=DESCRIPTIONS['NCBI-disease-IOB'], citation=CITATIONS['NCBI-disease-IOB'], homepage=HOMEPAGES['NCBI-disease-IOB']), BlurbConfig(name='JNLPBA', task='ner', label_classes=['O', 'B-protein', 'I-protein', 'B-cell_type', 'I-cell_type', 'B-cell_line', 'I-cell_line', 'B-DNA','I-DNA', 'B-RNA', 'I-RNA'], data_url = DATA_URL['JNLPBA'], description=DESCRIPTIONS['JNLPBA'], citation=CITATIONS['JNLPBA'], homepage=HOMEPAGES['JNLPBA']), BlurbConfig(name='BIOSSES', task='sent-sim', label_classes=None, data_url = DATA_URL['BIOSSES'], description=DESCRIPTIONS['BIOSSES'], citation=CITATIONS['BIOSSES'], homepage=HOMEPAGES['BIOSSES']), ] def _info(self): return datasets.DatasetInfo( description=self.config.description, features=self.config.features, supervised_keys=None, homepage=self.config.homepage, citation=self.config.citation, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" if self.config.task == 'ner': downloaded_files = dl_manager.download_and_extract(self.config.urls) return self._ner_split_generator(downloaded_files) if self.config.task == 'sent-sim': downloaded_file = dl_manager.download_and_extract(self.config.data_url) return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})] def _generate_examples(self, filepath): print("Before the download") logger.info("⏳ Generating examples from = %s", filepath) if self.config.task == 'ner': return self._ner_example_generator(filepath) if self.config.task == 'sent-sim': return self._sentsim_example_generator(filepath) def _ner_split_generator(self, downloaded_files): return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), ] def _ner_example_generator(self, filepath): with open(filepath, encoding="utf-8") as f: guid = 0 tokens = [] ner_tags = [] for line in f: if line == "" or line == "\n": if tokens: yield guid, { "id": str(guid), "tokens": tokens, "ner_tags": ner_tags, } guid += 1 tokens = [] ner_tags = [] else: # tokens are tab separated splits = line.split("\t") tokens.append(splits[0]) ner_tags.append(splits[1].rstrip()) # last example yield guid, { "id": str(guid), "tokens": tokens, "ner_tags": ner_tags, } def _sentsim_example_generator(self, filepath): """Yields examples as (key, example) tuples.""" df = pd.read_csv(filepath, sep="\t", encoding="utf-8") for idx, row in df.iterrows(): yield idx, { "sentence1": row["sentence1"], "sentence2": row["sentence2"], "score": row["score"], }