Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
License:
init
Browse files
README.md
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- **Paper:** [https://aclanthology.org/U15-1010.pdf](https://aclanthology.org/U15-1010.pdf)
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- **Dataset:** BioNLP2004
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- **Domain:** Biochemical
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- **Number of Entity:**
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### Dataset Summary
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BioNLP2004 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
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We take a half amount of test instances randomly from the training set and create a validation set with it.
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- Entity Types
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## Dataset Structure
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```
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{
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}
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```
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```python
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{
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"O": 0,
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"
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"I-
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"
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"I-
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}
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```
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| name |train|validation|test|
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|---------|----:|---------:|---:|
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|fin |
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### Citation Information
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- **Paper:** [https://aclanthology.org/U15-1010.pdf](https://aclanthology.org/U15-1010.pdf)
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- **Dataset:** BioNLP2004
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- **Domain:** Biochemical
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- **Number of Entity:** 5
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### Dataset Summary
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BioNLP2004 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
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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.
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- Entity Types: `DNA`, `protein`, `cell_type`, `cell_line`, `RNA`
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## Dataset Structure
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```
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{
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'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],
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'tokens': ['In', 'the', 'presence', 'of', 'Epo', ',', 'c-myb', 'mRNA', 'declined', 'and', '20', '%', 'of', 'K562', 'cells', 'synthesized', 'Hb', 'regardless', 'of', 'antisense', 'myb', 'RNA', 'expression', '.']
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}
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```
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```python
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{
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"O": 0,
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"B-DNA": 1,
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"I-DNA": 2,
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"B-protein": 3,
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"I-protein": 4,
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"B-cell_type": 5,
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"I-cell_type": 6,
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"B-cell_line": 7,
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"I-cell_line": 8,
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"B-RNA": 9,
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"I-RNA": 10
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}
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```
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| name |train|validation|test|
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|---------|----:|---------:|---:|
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|fin |16619 | 1927| 3856|
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### Citation Information
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