---
language: en
license: apache-2.0
tags:
- biomedical
- named-entity-recognition
- ner
- biomarkers
- bert
- bilstm
- crf
- biobbc
datasets:
- custom-biomarker-dataset
metrics:
- f1
- precision
- recall
widget:
- text: "Elevated levels of IL-6 and TNF-alpha were observed in patients."
- text: "Serum glucose and HbA1c levels were measured."
- text: "Cardiac troponin and BNP levels indicate myocardial injury."
---

# BioBBC: BERT-BiLSTM-CRF for Biomarker Recognition

## Model Description

BioBBC is an enhanced Named Entity Recognition (NER) model specifically trained for identifying biomarkers in biomedical text. It combines multiple state-of-the-art techniques:

- **BERT**: BioBERT for contextual embeddings
- **Character CNN**: Character-level features
- **POS Embeddings**: Part-of-speech information
- **Domain Embeddings**: Biomedical word embeddings
- **BiLSTM**: Bidirectional sequence modeling
- **CRF**: Conditional Random Fields for sequence labeling

## Model Architecture

```
Input Text
    ↓
BioBERT Embeddings (768d)
    ↓
+ Character CNN (150d)
+ POS Embeddings (25d)
+ Domain Embeddings (200d)
    ↓
BiLSTM (512d × 2)
    ↓
CRF Layer
    ↓
Predicted Biomarkers
```

## Performance

- **F1 Score**: 0.9734
- **Training Epochs**: 5
- **Best Epoch**: 5

## Labels

- `O`: Outside (not a biomarker)
- `B-BIOMARKER`: Beginning of biomarker entity
- `I-BIOMARKER`: Inside biomarker entity

## Usage

### Installation

```bash
pip install transformers torch huggingface_hub
```

### Load Model

```python
from huggingface_hub import hf_hub_download
import torch

# Download model
model_path = hf_hub_download(repo_id="postlyt/biobbc-biomarker-ner", filename="pytorch_model.bin")
config_path = hf_hub_download(repo_id="postlyt/biobbc-biomarker-ner", filename="config.json")
vocab_path = hf_hub_download(repo_id="postlyt/biobbc-biomarker-ner", filename="vocabularies.bin")

# Load model (see full loading script in repository)
# model = load_biobbc_model(model_path, config_path, vocab_path)
```

### Predict Biomarkers

```python
# See predict_from_huggingface.py in repository for complete example
text = "Elevated IL-6 and TNF-alpha levels were observed."
biomarkers = model.predict(text)
print(biomarkers)  # ['IL-6', 'TNF-alpha']
```

## Training Details

- **Base Model**: dmis-lab/biobert-base-cased-v1.2
- **Training Data**: Custom biomarker dataset
- **Batch Size**: 256 (optimized for A100 GPU)
- **Mixed Precision**: FP16 enabled
- **Optimization**: AdamW with linear warmup

## Citation

If you use this model, please cite:

```bibtex
@misc{biobbc_model,
  author = {postlyt},
  title = {BioBBC: BERT-BiLSTM-CRF for Biomarker Recognition},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/postlyt/biobbc-biomarker-ner}
}
```

## Model Card Authors

postlyt

## Model Card Contact

For questions or issues, please open an issue in the repository.