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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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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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_DESCRIPTION = """\ |
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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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class BC4CHEMDConfig(datasets.BuilderConfig): |
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"""BuilderConfig for BC4CHEMD""" |
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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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class BC4CHEMD(datasets.GeneratorBasedBuilder): |
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""" BC4CHEMD dataset.""" |
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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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def _info(self): |
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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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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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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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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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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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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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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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} |
