import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @article{smith2008overview, title={Overview of BioCreative II gene mention recognition}, author={Smith, Larry and Tanabe, Lorraine K and nee Ando, Rie Johnson and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M and Ganchev, Kuzman and others}, journal={Genome biology}, volume={9}, number={S2}, pages={S2}, year={2008}, publisher={Springer} }""" _DESCRIPTION = """\ Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions. For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/ The original dataset can be downloaded from: https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-ii-corpus/ This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll """ _HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/" _URL = "https://github.com/spyysalo/bc2gm-corpus/raw/master/conll/" _TRAINING_FILE = "train.tsv" _DEV_FILE = "devel.tsv" _TEST_FILE = "test.tsv" class Bc2gmCorpusConfig(datasets.BuilderConfig): """BuilderConfig for Bc2gmCorpus""" def __init__(self, **kwargs): """BuilderConfig for Bc2gmCorpus. Args: **kwargs: keyword arguments forwarded to super. """ super(Bc2gmCorpusConfig, self).__init__(**kwargs) class Bc2gmCorpus(datasets.GeneratorBasedBuilder): """Bc2gmCorpus dataset.""" BUILDER_CONFIGS = [ Bc2gmCorpusConfig(name="bc2gm_corpus", version=datasets.Version("1.0.0"), description="bc2gm corpus"), ] def _info(self): custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE', 'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE', 'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES'] return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=custom_names ) ), } ), supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" urls_to_download = { "train": f"{_URL}{_TRAINING_FILE}", "dev": f"{_URL}{_DEV_FILE}", "test": f"{_URL}{_TEST_FILE}", } downloaded_files = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), ] def _generate_examples(self, filepath): shift = 0 logger.info("⏳ Generating examples from = %s", 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, } # Bc2gmCorpus()