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Upload BC5CDR.py

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  1. BC5CDR.py +125 -0
BC5CDR.py ADDED
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+ import datasets
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+
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+
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+ logger = datasets.logging.get_logger(__name__)
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+
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+
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+ _CITATION = """\
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+ @article{krallinger2015chemdner,
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+ title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
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+ 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},
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+ journal={Journal of cheminformatics},
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+ volume={7},
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+ number={1},
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+ pages={1--17},
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+ year={2015},
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+ publisher={BioMed Central}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ """
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+
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+ _HOMEPAGE = ""
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+ _URL = "https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/BC5CDR-IOB/"
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+ _TRAINING_FILE = "train.tsv"
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+ _DEV_FILE = "devel.tsv"
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+ _TEST_FILE = "test.tsv"
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+
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+
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+ class BC4CHEMDConfig(datasets.BuilderConfig):
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+ """BuilderConfig for BC4CHEMD"""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for BC4CHEMD.
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+ Args:
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(BC4CHEMDConfig, self).__init__(**kwargs)
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+
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+
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+ class BC4CHEMD(datasets.GeneratorBasedBuilder):
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+ """ BC4CHEMD dataset."""
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+
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+ BUILDER_CONFIGS = [
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+ BC4CHEMDConfig(name="BC5CDR-Disease", version=datasets.Version("1.0.0"), description=" BC5CDR-Disease dataset"),
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+ ]
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+
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+ def _info(self):
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+
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+ custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
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+ 'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE',
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+ 'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES']
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+
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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=custom_names
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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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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ urls_to_download = {
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+ "train": f"{_URL}{_TRAINING_FILE}",
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+ "dev": f"{_URL}{_DEV_FILE}",
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+ "test": f"{_URL}{_TEST_FILE}",
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+ }
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+ downloaded_files = dl_manager.download_and_extract(urls_to_download)
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
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+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ logger.info("⏳ Generating examples from = %s", 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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+ print(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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+ # tokens are tab separated
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+ splits = line.split("\t")
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+ tokens.append(splits[0])
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+ if(splits[1].rstrip()=="B-Chemical"):
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+ ner_tags.append("B-CHEMICAL")
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+ elif(splits[1].rstrip()=="I-Chemical"):
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+ ner_tags.append("I-CHEMICAL")
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+ elif(splits[1].rstrip()=="B-Disease"):
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+ ner_tags.append("B-DISEASE")
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+ elif(splits[1].rstrip()=="I-Disease"):
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+ ner_tags.append("I-DISEASE")
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+ else:
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+ ner_tags.append("O")
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+ # ner_tags.append(splits[1].rstrip())
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+ # last example
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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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+ }