README.md

---
language: en
tags:
- topic-drift
- conversation-analysis
- pytorch
- attention
license: mit
datasets:
- leonvanbokhorst/topic-drift-v2
metrics:
- rmse
- r2_score
model-index:
- name: topic-drift-detector
  results:
  - task: 
      type: topic-drift-detection
      name: Topic Drift Detection
    dataset:
      name: leonvanbokhorst/topic-drift-v2
      type: conversations
    metrics:
      - name: Test RMSE
        type: rmse
        value: 0.0144
      - name: Test R²
        type: r2
        value: 0.8666
      - name: Test Loss 
        type: loss
        value: 0.0002
---

# Topic Drift Detector Model

## Version: v20241226_105737

This model detects topic drift in conversations using a streamlined attention-based architecture. Trained on the [leonvanbokhorst/topic-drift-v2](https://huggingface.co/datasets/leonvanbokhorst/topic-drift-v2) dataset.

## Model Architecture
- Efficient single-layer attention mechanism
- Direct pattern recognition
- Streamlined processing pipeline
- Optimized scaling factor (4.0)
- PreNorm layers with residual connections

### Key Components:
1. **Embedding Processor**:
   - Input dimension: 1024
   - Hidden dimension: 512
   - Dropout rate: 0.35
   - PreNorm layers with residual connections

2. **Attention Block**:
   - Single attention layer
   - Feed-forward dimension: 512
   - Learned position encodings
   - Residual connections

3. **Pattern Recognition**:
   - Direct feature extraction
   - Efficient tensor operations
   - Optimized memory usage

## Performance Metrics
```txt
=== Full Training Results ===
Best Validation RMSE: 0.0142
Best Validation R²: 0.8711

=== Test Set Results ===
Loss: 0.0002
RMSE: 0.0144
R²: 0.8666
```

## Training Details
- Dataset: 6400 conversations (5120 train, 640 val, 640 test)
- Window size: 8 turns
- Batch size: 32
- Learning rate: 0.0001
- Early stopping patience: 15
- Distribution regularization weight: 0.1
- Target standard deviation: 0.2
- Base embeddings: BAAI/bge-m3

## Key Improvements
1. **Simplified Architecture**:
   - Reduced complexity
   - Focused pattern detection
   - Efficient processing
   - Optimized memory usage

2. **Performance Benefits**:
   - Improved RMSE (0.0144)
   - Strong R² score (0.8666)
   - Consistent predictions
   - Wide score range

## Usage Example
```python
import torch
from transformers import AutoModel, AutoTokenizer

# Load base embedding model
base_model = AutoModel.from_pretrained('BAAI/bge-m3')
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-m3')

# Load topic drift detector
model = torch.load('models/v20241226_105737/topic_drift_model.pt')
model.eval()

# Prepare conversation window (8 turns)
conversation = [
    "How was your weekend?",
    "It was great! Went hiking.",
    "Which trail did you take?",
    "The mountain loop trail.",
    "That's nice. By the way, did you watch the game?",
    "Yes! What an amazing match!",
    "The final score was incredible.",
    "I couldn't believe that last-minute goal."
]

# Get embeddings
with torch.no_grad():
    inputs = tokenizer(conversation, padding=True, truncation=True, return_tensors='pt')
    embeddings = base_model(**inputs).last_hidden_state.mean(dim=1)  # [8, 1024]
    
    # Reshape for model input [1, 8*1024]
    conversation_embeddings = embeddings.view(1, -1)
    
    # Get drift score
    drift_scores = model(conversation_embeddings)
    
print(f"Topic drift score: {drift_scores.item():.4f}")
# Higher scores indicate more topic drift
```

## Limitations
- Works best with English conversations
- Requires exactly 8 turns of conversation
- Each turn should be between 1-512 tokens
- Relies on BAAI/bge-m3 embeddings

## Training Curves
![Training Curves](plots/v20241226_105737/training_curves.png)