README.md

---
datasets:
- leonvanbokhorst/topic-drift
---

# Topic Drift Detector Model

## Version: v20241225_085248

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
![Training Curves](plots/v20241225_085248/training_curves.png)

## Usage
```python
import torch

# Load model
model = torch.load('models/v20241225_085248/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)
```