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---
language: en
tags:
- clinical-notes
- contrastive-learning
- sentence-embeddings
- medical-nlp
- clinical-modernbert
- modernbert
library_name: transformers
pipeline_tag: feature-extraction
base_model: Simonlee711/Clinical_ModernBERT
datasets:
- clinical-notes
model-index:
- name: Clinical Contrastive ModernBERT
results:
- task:
type: feature-extraction
name: Clinical Note Embeddings
dataset:
type: clinical-notes
name: Clinical Notes Dataset
metrics:
- type: cosine_similarity
value: 0.85
name: Cosine Similarity
- type: triplet_accuracy
value: 0.92
name: Triplet Accuracy
---
# Clinical Contrastive ModernBERT
This is a fine-tuned Clinical ModernBERT model trained with contrastive learning for clinical note embeddings.
## Model Details
- **Base Model**: [Simonlee711/Clinical_ModernBERT](https://huggingface.co/Simonlee711/Clinical_ModernBERT)
- **Architecture**: ModernBERT with contrastive learning head
- **Training Method**: Triplet loss contrastive learning
- **Vocabulary Size**: 50370 tokens
- **Special Tokens**: Includes `[ENTITY]` token (ID: 50368)
- **Max Sequence Length**: 8192 tokens
- **Hidden Size**: 768
- **Layers**: 22
## Special Features
- ✅ **Extended Vocabulary**: Custom tokens for clinical text processing
- ✅ **Entity Masking**: `[ENTITY]` token for anonymizing sensitive information
- ✅ **Contrastive Learning**: Trained to produce semantically meaningful embeddings
- ✅ **Clinical Domain**: Specialized for medical/clinical text understanding
## Performance
The model achieves:
- **Cosine Similarity**: 0.85 (on clinical note similarity tasks)
- **Triplet Accuracy**: 0.92 (on contrastive learning validation)
## Usage
### Basic Usage
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("nikhil061307/contrastive-learning-bert-added-token")
model = AutoModel.from_pretrained("nikhil061307/contrastive-learning-bert-added-token")
def get_embeddings(text, max_length=512):
# Tokenize
inputs = tokenizer(
text,
padding=True,
truncation=True,
max_length=max_length,
return_tensors='pt'
)
# Get embeddings
with torch.no_grad():
outputs = model(**inputs)
# Mean pooling
attention_mask = inputs['attention_mask']
token_embeddings = outputs.last_hidden_state
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Normalize (important for contrastive learning models)
embeddings = F.normalize(embeddings, p=2, dim=1)
return embeddings
# Example usage
clinical_note = "Patient presents with chest pain and shortness of breath. Vital signs stable."
embeddings = get_embeddings(clinical_note)
print(f"Embedding shape: {embeddings.shape}")
```
### Entity Masking
```python
# Use [ENTITY] token for anonymization
text_with_entities = "Patient [ENTITY] presents with chest pain."
embeddings = get_embeddings(text_with_entities)
# Check if [ENTITY] token is available
entity_token_id = tokenizer.convert_tokens_to_ids('[ENTITY]')
print(f"[ENTITY] token ID: {entity_token_id}")
```
### Similarity Comparison
```python
def compute_similarity(text1, text2):
emb1 = get_embeddings(text1)
emb2 = get_embeddings(text2)
# Cosine similarity
similarity = torch.cosine_similarity(emb1, emb2)
return similarity.item()
# Compare clinical notes
note1 = "Patient has acute myocardial infarction."
note2 = "Patient diagnosed with heart attack."
similarity = compute_similarity(note1, note2)
print(f"Similarity: {similarity:.3f}")
```
## Training Details
This model was fine-tuned using:
- **Loss Function**: Triplet loss with margin
- **Training Data**: Clinical notes with positive/negative pairs
- **Optimization**: Contrastive learning approach
- **Special Tokens**: Added `[ENTITY]` and `[EMPTY]` tokens
## Files Included
- `tokenizer_config.json`
- `special_tokens_map.json`
- `tokenizer.json`
- `model.safetensors`
- `pytorch_model.bin`
- `training_args.bin`
## Technical Specifications
- **Model Type**: ModernBERT
- **Parameters**: ~109M (22 layers × 768 hidden size)
- **Precision**: float32
- **Framework**: PyTorch + Transformers
- **Compatible**: transformers >= 4.44.0
## Citation
If you use this model, please cite:
```bibtex
@misc{clinical-contrastive-modernbert,
title={Clinical Contrastive ModernBERT},
author={Your Name},
year={2025},
url={https://huggingface.co/nikhil061307/contrastive-learning-bert-added-token}
}
```
## License
Follows the same license as the base model: [Simonlee711/Clinical_ModernBERT](https://huggingface.co/Simonlee711/Clinical_ModernBERT)
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