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# Gene Extraction Model
This model is fine-tuned for gene extraction using BERT-CRF architecture.
## Model Description
This model uses a custom BERT-CRF architecture for token classification, specifically designed for gene entity recognition. The model combines BERT with a Conditional Random Field (CRF) layer for improved sequence labeling.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
model_name = "RaduGabriel/gene-entity-recognition"
hf_token = None
tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
model = AutoModelForTokenClassification.from_pretrained(model_name, token=hf_token)
text = "TIF1gamma, a novel member of the transcriptional intermediary factor 1 family, plays a crucial role in gene regulation."
# Create NER pipeline
ner_pipeline = pipeline(
"ner",
model=model,
tokenizer=tokenizer,
aggregation_strategy="simple"
)
results = ner_pipeline(text)
print(results)
```
## Labels
- O
- B-GENE
- I-GENE
## Model Details
- Architecture: BERT-CRF
- Base Model: dmis-lab/biobert-v1.1
- Number of Labels: 3
- CRF Layer: Enabled
## Training Details
- Training Data: GNormPlus dataset
- Optimizer: AdamW
- Learning Rate: 2e-05
- Batch Size: 32
- Epochs: 3
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