BLURB.py

"""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
from constants import CITATIONS, DESCRIPTIONS, HOMEPAGES, DATA_URL

_CITATION = """\
@article{2022,
   title={Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
   volume={3},
   ISSN={2637-8051},
   url={http://dx.doi.org/10.1145/3458754},
   DOI={10.1145/3458754},
   number={1},
   journal={ACM Transactions on Computing for Healthcare},
   publisher={Association for Computing Machinery (ACM)},
   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},
   year={2022},
   month={Jan},
   pages={1–23} 
   }
"""

_DESCRIPTION = """BLURB (Biomedical Language Understanding and Reasoning Benchmark.)
is a comprehensive benchmark for biomedical NLP, with 13 biomedical NLP datasets in 6 
tasks (NER, PICO, Relation Extraction, Sentence similarity, document classification, question answering). 
Our aim is to facilitate investigations of biomedical natural language processing 
with a specific focus on language model pretraining and to help accelerate progress in universal Biomedical 
NLP applications. The table below compares the datasets comprising BLURB versus the various datasets used in 
previous Biomedical and Clinical BERT language models."""

_HOMEPAGE = "https://microsoft.github.io/BLURB/index.html"

_LICENSE = "TBD"


_VERSION = "1.0.0"

DATA_DIR = "blurb/"

logger = datasets.logging.get_logger(__name__)

CITATION_BC5_CHEM = """@article{article,
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},
year = {2016},
month = {05},
pages = {baw068},
title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
volume = {2016},
journal = {Database},
doi = {10.1093/database/baw068}
}
"""
CITATION_BC2_GENE = """@article{article,
author = {Smith, Larry and Tanabe, Lorraine and Ando, Rie and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig and Povinelli, Richard and Vlachos, Andreas and Baumgartner Jr, William and Hunter, Lawrence and Carpenter, Bob and Wilbur, W.},
year = {2008},
month = {09},
pages = {S2},
title = {Overview of BioCreative II gene mention recognition},
volume = {9 Suppl 2},
journal = {Genome biology},
doi = {10.1186/gb-2008-9-s2-s2}
}"""

class BlurbConfig(datasets.BuilderConfig):
    """BuilderConfig for BLURB."""

    def __init__(self, task, data_url, citation, homepage, label_classes=("False", "True"), **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'}"
                        }


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 = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
                    description="""The corpus consists of three separate sets of 
                    articles with diseases, chemicals and their relations annotated. 
                    The training (500 articles) and development (500 articles) sets 
                    were released to task participants in advance to support text-mining 
                    method development. The test set (500 articles) was used for final 
                    system performance evaluation.""",
                    citation=CITATION_BC5_CHEM,
                    homepage="https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus"),

    BlurbConfig(name='BC5CDR-disease-IOB', task='ner', label_classes=['O', 'B-Disease', 'I-Disease'],
                    data_url = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
                    description="""The corpus consists of three separate sets of 
                    articles with diseases, chemicals and their relations annotated. 
                    The training (500 articles) and development (500 articles) sets 
                    were released to task participants in advance to support text-mining 
                    method development. The test set (500 articles) was used for final 
                    system performance evaluation.""",
                    citation=CITATION_BC5_CHEM,
                    homepage="https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus"),

    BlurbConfig(name='BC2GM-IOB', task='ner', label_classes=['O', 'B-GENE', 'I-GENE'],
                    data_url = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/",
                    description="""The BioCreative II Gene Mention task.
                    The training corpus for the current task consists mainly of 
                    the training and testing corpora (text collections) from the 
                    BCI task, and the testing corpus for the current task 
                    consists of an additional 5,000 sentences that were held 
                    'in reserve' from the previous task.
                    In the current corpus, tokenization is not provided; 
                    instead participants are asked to identify a gene mention 
                    in a sentence by giving its start and end characters. 
                    As before, the training set consists of a set of sentences, 
                    and for each sentence a set of gene mentions 
                    (GENE annotations).
                    """,
                    citation=CITATION_BC2_GENE,
                    homepage="https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/"),
                   
  ]


  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)


  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)

  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,
        }