
# Dataset Card for "tner/bc5cdr"

## Dataset Description

- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true)
- **Dataset:** BioCreative V CDR
- **Domain:** Biomedical
- **Number of Entity:** 2

### Dataset Summary
BioCreative V CDR NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.
- Entity Types: `Chemical`, `Disease`

## 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](https://huggingface.co/datasets/tner/bc5cdr/raw/main/dataset/label.json).
```python
{
    "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}
}
```
task_categories:token-classification task_ids:named-entity-recognition multilinguality:monolingual size_categories:10K<n<100K language:en license:other region:us
{"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "BioCreative V CDR"}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-other #region-us 

Dataset Card for "tner/bc5cdr"
==============================


Dataset Description
-------------------


* Repository: T-NER
* Paper: URL
* Dataset: BioCreative V CDR
* Domain: Biomedical
* Number of Entity: 2


### Dataset Summary


BioCreative V CDR NER dataset formatted in a part of TNER project.
The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.


* Entity Types: 'Chemical', 'Disease'


Dataset Structure
-----------------


### Data Instances


An example of 'train' looks as follows.


### Label ID


The label2id dictionary can be found at here.


### Data Splits




### Dataset Summary


BioCreative V CDR NER dataset formatted in a part of TNER project.
The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.


* Entity Types: 'Chemical', 'Disease'


Dataset Structure
----------------- ### Data Instances


An example of 'train' looks as follows. ### Label ID


The label2id dictionary can be found at here. ### Data Splits
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-monolingual #size_categories-10K<n<100K #language-English #license-other #region-us 
 ### Dataset Summary


BioCreative V CDR NER dataset formatted in a part of TNER project.
The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.


* Entity Types: 'Chemical', 'Disease'


Dataset Structure
----------------- ### Data Instances


An example of 'train' looks as follows. ### Label ID


The label2id dictionary can be found at here. ### Data Splits