Mardiyyah/bioformer-ner-model
Token Classification
•
Updated
•
45
tokens
sequence | tags
sequence |
---|---|
[
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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.
DNA
, protein
, cell_type
, cell_line
, RNA
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', '.']
}
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
}
name | train | validation | test |
---|---|---|---|
bionlp2004 | 16619 | 1927 | 3856 |
@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",
}