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# Topic Drift Detector Model
## Version: v20241225_090318
This model detects topic drift in conversations using an enhanced attention-based architecture.
## Model Architecture
- Multi-head attention mechanism
- Bidirectional LSTM for pattern detection
- Dynamic weight generation
- Semantic bridge detection
## Performance Metrics
```txt
=== Full Training Results ===
Best Validation RMSE: 0.0107
Best Validation R²: 0.8867
=== Test Set Results ===
Loss: 0.0002
RMSE: 0.0129
R²: 0.8373
```
## Training Curves
## Usage
```python
import torch
# Load model
model = torch.load('models/v20241225_090318/topic_drift_model.pt')
# Use model for inference
# Input shape: [batch_size, sequence_length * embedding_dim]
# Output shape: [batch_size, 1] (drift score between 0 and 1)
```
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