Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
License:
metadata
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Ontonotes5
Dataset Card for "tner/ontonotes5"
Dataset Description
- Repository: T-NER
- Paper: https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true
Dataset Summary
Ontonotes5 NER dataset formatted in a part of TNER project.
Dataset Structure
Data Instances
An example of train
looks as follows.
{
'tags': [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],
'tokens': ['Fasciculations', 'in', 'six', 'areas', 'of', 'the', 'body', 'were', 'scored', 'from', '0', 'to', '3', 'and', 'summated', 'as', 'a', 'total', 'fasciculation', 'score', '.']
}
Label ID
The label2id dictionary can be found at here.
{
"O": 0,
"B-Chemical": 1,
"B-Disease": 2,
"I-Disease": 3,
"I-Chemical": 4
}
Data Splits
name | train | validation | test |
---|---|---|---|
bc5cdr | 5228 | 5330 | 5865 |
Citation Information
@article{wei2016assessing,
title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task},
author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and Lu, Zhiyong},
journal={Database},
volume={2016},
year={2016},
publisher={Oxford Academic}
}