Contra-Sformer

# Contra-Sformer Implementation Guide

## Step 1: Data Preparation
- Collect robotic surgery data from [COMPANY]'s platform
- Ensure data includes kinematic, visual, and expert feedback streams
- Preprocess data to match Contra-Sformer input requirements

## Step 2: Model Architecture
```python
class ContraSformer(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_heads, num_layers):
        super().__init__()
        self.encoder = TransformerEncoder(...)
        self.contrastive_head = nn.Linear(...)
        self.regression_head = nn.Linear(...)
```

## Step 3: Training
- Use contrastive loss for skill assessment
- Combine with regression loss for performance metrics
- Implement early stopping based on validation performance

## Step 4: Deployment
- Deploy model to [COMPANY]'s robotic surgery platform
- Set up real-time skill assessment pipeline
- Implement feedback loop for continuous improvement