safetensors bert base_model:michiyasunaga/BioLinkBERT-large base_model:finetune:michiyasunaga/BioLinkBERT-large region:us
---
base_model:
- michiyasunaga/BioLinkBERT-large
---
# Healthcare Interoperability NER Model
This model is fine-tuned to identify healthcare interoperability concepts in biomedical text.

## Model Details
- **Base model:** BioLinkBERT-large with domain adaptation on healthcare interoperability literature
- **Task:** Named Entity Recognition (NER)
- **Training data:** Annotated corpus of healthcare interoperability concepts
- **Labels:** 
  - O: Not an entity
  - B-ENTITY: Beginning of interoperability entity
  - I-ENTITY: Inside/continuation of interoperability entity

## Evaluation Results
Our model achieves the following performance on healthcare interoperability entity recognition:

| Metric | Score |
|--------|-------|
| F1 Score | 74.4% |
| Precision | 75.4% |
| Recall | 73.3% |
| Accuracy | 94.3% |

**Improvement over base model:**
| Model | F1 Score | Precision | Recall |
|-------|----------|-----------|--------|
| Base BioLinkBERT | 65.9% | 66.7% | 65.2% |
| + Domain Adaptation | 74.4% | 75.4% | 73.3% |
| **Improvement** | **+8.5%** | **+8.7%** | **+8.1%** |

**Example model outputs:**

Input: "FHIR and HL7 standards improve healthcare data interoperability."

Identified entities:
- FHIR
- HL7 standards
- healthcare data interoperability

## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("adamleeit/biolink-lg-interop-ner-model")
model = AutoModelForTokenClassification.from_pretrained("adamleeit/biolink-lg-interop-ner-model")
# Tokenize input text
text = "FHIR and HL7 standards improve healthcare data interoperability."
inputs = tokenizer(text, return_tensors="pt", return_offsets_mapping=True)
offset_mapping = inputs.pop("offset_mapping")
# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
# Process predictions
predictions = torch.argmax(outputs.logits, dim=2)
input_ids = inputs["input_ids"][0]
# Convert predictions to entities
predicted_entities = []
current_entity = []
for i, pred in enumerate(predictions[0]):
    token = tokenizer.convert_ids_to_tokens(input_ids[i])
    if pred == 0:  # O
        if current_entity:
            predicted_entities.append(" ".join(current_entity))
            current_entity = []
    elif pred == 1:  # B-ENTITY
        if current_entity:
            predicted_entities.append(" ".join(current_entity))
        current_entity = [token]
    elif pred == 2:  # I-ENTITY
        current_entity.append(token)
if current_entity:
    predicted_entities.append(" ".join(current_entity))
print(f"Identified entities: {predicted_entities}")
```
Citation
If you use this model in your research, please cite:
```
@misc{interoperability-ner-model,
  author = {Your Name},
  title = {Healthcare Interoperability NER Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/adamleeit/biolink-lg-interop-ner-model}}
}
```