README.md

---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: BioNLP2004
---

# Dataset Card for "tner/bionlp2004"

## Dataset Description

- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/U15-1010.pdf](https://aclanthology.org/U15-1010.pdf)
- **Dataset:** BioNLP2004
- **Domain:** Biochemical
- **Number of Entity:** 4

### Dataset Summary
BioNLP2004 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
Original BioNLP2004 dataset contains training and test.
We take a half amount of test instances randomly from the training set and create a validation set with it.

- Entity Types:`ORG`, `LOC`, `PER`, `MISC`

## Dataset Structure

### Data Instances
An example of `train` looks as follows.

```
{
    "tags": [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    "tokens": ["1", ".", "1", ".", "4", "Borrower", "engages", "in", "criminal", "conduct", "or", "is", "involved", "in", "criminal", "activities", ";"]
}
```

### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/fin/raw/main/dataset/label.json).
```python
{
    "O": 0,
    "I-ORG": 1,
    "I-LOC": 2,
    "I-PER": 3,
    "I-MISC": 4
}
```

### Data Splits

|  name   |train|validation|test|
|---------|----:|---------:|---:|
|fin      |861  |       303| 303|

### 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",
}
```