README.md

metadata

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

Topic Drift Detector Model

Version: v20241226_114030

This model detects topic drift in conversations using an efficient attention-based architecture. Trained on the leonvanbokhorst/topic-drift-v2 dataset.

Model Architecture

Key Components:

1. Input Processing:

   • Input dimension: 1024 (BGE-M3 embeddings)
   • Hidden dimension: 512
   • Sequence length: 8 turns

2. Attention Block:

   • Multi-head attention (4 heads)
   • PreNorm layers with residual connections
   • Dropout rate: 0.1

3. Feed-Forward Network:

   • Two-layer MLP with GELU activation
   • Hidden dimension: 512 -> 2048 -> 512
   • Residual connections

4. Output Layer:

   • Two-layer MLP: 512 -> 256 -> 1
   • GELU activation
   • Direct sigmoid output for [0,1] range

Performance Metrics

=== 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

Usage Example

# Install dependencies
pip install torch transformers huggingface_hub

# Import required packages
import torch
from transformers import AutoModel, AutoTokenizer
from huggingface_hub import hf_hub_download

# Load base model and tokenizer
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
base_model = AutoModel.from_pretrained('BAAI/bge-m3').to(device)
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-m3')

# Download and load topic drift model
model_path = hf_hub_download(
    repo_id='leonvanbokhorst/topic-drift-detector',
    filename='models/v20241226_114030/topic_drift_model.pt'
)
checkpoint = torch.load(model_path, weights_only=True, map_location=device)
model = EnhancedTopicDriftDetector(
    input_dim=1024,
    hidden_dim=checkpoint['hyperparameters']['hidden_dim']
).to(device)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Example conversation
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."
]

# Process conversation
with torch.no_grad():
    # Get embeddings
    inputs = tokenizer(conversation, padding=True, truncation=True, return_tensors='pt')
    inputs = dict((k, v.to(device)) for k, v in inputs.items())
    embeddings = base_model(**inputs).last_hidden_state.mean(dim=1)
    
    # Get drift score
    conversation_embeddings = embeddings.view(1, -1)
    drift_score = model(conversation_embeddings)
    print(f"Topic drift score: {drift_score.item():.4f}")

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