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sequence
tags
sequence
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Dataset Card for "tner/bionlp2004"

Dataset Summary

BioNLP2004 NER dataset formatted in a part of TNER project. BioNLP2004 dataset contains training and test only, so we randomly sample a half size of test instances from the training set to create validation set.

  • Entity Types: DNA, protein, cell_type, cell_line, RNA

Dataset Structure

Data Instances

An example of train looks as follows.

{
    'tags': [0, 0, 0, 0, 3, 0, 9, 10, 0, 0, 0, 0, 0, 7, 8, 0, 3, 0, 0, 9, 10, 10, 0, 0],
    'tokens': ['In', 'the', 'presence', 'of', 'Epo', ',', 'c-myb', 'mRNA', 'declined', 'and', '20', '%', 'of', 'K562', 'cells', 'synthesized', 'Hb', 'regardless', 'of', 'antisense', 'myb', 'RNA', 'expression', '.']
}

Label ID

The label2id dictionary can be found at here.

{
    "O": 0,
    "B-DNA": 1,
    "I-DNA": 2,
    "B-protein": 3,
    "I-protein": 4,
    "B-cell_type": 5,
    "I-cell_type": 6,
    "B-cell_line": 7,
    "I-cell_line": 8,
    "B-RNA": 9,
    "I-RNA": 10
}

Data Splits

name train validation test
bionlp2004 16619 1927 3856

Citation Information

@inproceedings{collier-kim-2004-introduction,
    title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
    author = "Collier, Nigel  and
      Kim, Jin-Dong",
    booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
    month = aug # " 28th and 29th",
    year = "2004",
    address = "Geneva, Switzerland",
    publisher = "COLING",
    url = "https://aclanthology.org/W04-1213",
    pages = "73--78",
}
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Models trained or fine-tuned on tner/bionlp2004