# Gene Extraction Model

This model is fine-tuned for gene extraction using BERT-CRF architecture.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

# Load model and tokenizer
model_name = "RaduGabriel/gene-entity-recognition"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Create NER pipeline
ner_pipeline = pipeline(
    "ner",
    model=model,
    tokenizer=tokenizer,
    aggregation_strategy="simple"
)

# Example usage
text = "The BRCA1 gene is associated with breast cancer."
results = ner_pipeline(text)
```

## Labels
- O
- B-GENE
- I-GENE
- E-GENE
- S-GENE

## Model Details
- Architecture: BERT-CRF
- Base Model: answerdotai/ModernBERT-large
- Number of Labels: 5
- CRF Layer: Enabled