import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @article{krallinger2015chemdner, title={The CHEMDNER corpus of chemicals and drugs and its annotation principles}, author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others}, journal={Journal of cheminformatics}, volume={7}, number={1}, pages={1--17}, year={2015}, publisher={BioMed Central} } """ _DESCRIPTION = """\ """ _HOMEPAGE = "" _URL = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/BC5CDR-IOB/" _TRAINING_FILE = "train.tsv" _DEV_FILE = "devel.tsv" _TEST_FILE = "test.tsv" class BC4CHEMDConfig(datasets.BuilderConfig): """BuilderConfig for BC4CHEMD""" def __init__(self, **kwargs): """BuilderConfig for BC4CHEMD. Args: **kwargs: keyword arguments forwarded to super. """ super(BC4CHEMDConfig, self).__init__(**kwargs) class BC4CHEMD(datasets.GeneratorBasedBuilder): """ BC4CHEMD dataset.""" BUILDER_CONFIGS = [ BC4CHEMDConfig(name="BC5CDR-Disease", version=datasets.Version("1.0.0"), description=" BC5CDR-Disease dataset"), ] 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): 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: print(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]) if(splits[1].rstrip()=="B-Chemical"): ner_tags.append("B-CHEMICAL") elif(splits[1].rstrip()=="I-Chemical"): ner_tags.append("I-CHEMICAL") elif(splits[1].rstrip()=="B-Disease"): ner_tags.append("B-DISEASE") elif(splits[1].rstrip()=="I-Disease"): ner_tags.append("I-DISEASE") else: ner_tags.append("O") # ner_tags.append(splits[1].rstrip()) # last example yield guid, { "id": str(guid), "tokens": tokens, "ner_tags": ner_tags, }