---
license: mit
task_categories:
- token-classification
task_ids:
- named-entity-recognition
dataset_info:
  features:
  - name: tags
    list: string
  - name: tokens
    list: string
  splits:
  - name: train
    num_bytes: 6624398
    num_examples: 16619
  - name: test
    num_bytes: 1526290
    num_examples: 3856
  - name: validation
    num_bytes: 757865
    num_examples: 1927
  download_size: 1759835
  dataset_size: 8908553
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
language:
- en
---

---



# Dataset Card for BioNLP2004 Filtered

This dataset is an adaptation of the original [tner/bionlp2004](https://huggingface.co/datasets/tner/bionlp2004) dataset, specifically tailored for our specific project use-case which focuses on `CellLine` and `CellType` entities.

## Dataset Description

The original BioNLP2004 dataset is a named entity recognition (NER) dataset in the biomedical domain, annotated with various entity types such as DNA, Protein, Cell_type, Cell_line, and RNA.

This adapted version, `OTAR3088/BioNLP_Filtered`, has undergone the following modifications:

1.  **Entity Filtering**: The dataset has been filtered to retain only `CellLine` and `CellType` entities. All other original entity types (DNA, Protein, RNA) have been converted to the 'O' (Outside) tag.
2.  **Nomenclature Change**: The naming convention for the retained entities has been updated to align with project-specific styling. For instance, `cell_line` entities from the original dataset are now represented as `CellLine`, and `cell_type` as `CellType`.

## Dataset Structure

The dataset consists of three splits: `train`, `test`, and `validation`.

Each example in the dataset contains:

*   `tokens`: A list of strings representing the tokens in a sentence.
*   `tags`: A list of strings representing the IOB (Inside, Outside, Beginning) tags for each token. The tags are now limited to 'O', 'B-CellLine', 'I-CellLine', 'B-CellType', and 'I-CellType'.

### Example

```json
{
  "tags": [
    "O",
    "B-CellLine",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O"
  ],
  "tokens": [
    "Since",
    "HUVECs",
    "released",
    "superoxide",
    "anions",
    "in",
    "response",
    "to",
    "TNF",
    ",",
    "and",
    "H2O2",
    "induces",
    "VCAM-1",
    ",",
    "PDTC",
    "may",
    "act",
    "as",
    "a",
    "radical",
    "scavenger",
    "."
  ]
}