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README.md

@@ -34,7 +34,7 @@ model-index:
 
 # Topic Drift Detector Model
 
-## Version: v20241226_105942
+## Version: v20241226_110212
 
 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.
 
@@ -99,19 +99,47 @@ R²: 0.8666
    - Wide score range
 
 ## Usage Example
+
+To use the model, first install the required packages:
+```bash
+pip install torch transformers huggingface_hub
+```
+
+Then use the following code:
 ```python
 import torch
 from transformers import AutoModel, AutoTokenizer
+from huggingface_hub import hf_hub_download
+
+def load_model(repo_id: str = "leonvanbokhorst/topic-drift-detector"):
+    # Download latest model weights
+    model_path = hf_hub_download(
+        repo_id=repo_id,
+        filename="models/latest/topic_drift_model.pt"
+    )
+    
+    # Load checkpoint
+    checkpoint = torch.load(model_path, weights_only=True)
+    
+    # Create model with same hyperparameters
+    model = EnhancedTopicDriftDetector(
+        input_dim=1024,  # BGE-M3 embedding dimension
+        hidden_dim=checkpoint['hyperparameters']['hidden_dim']
+    )
+    
+    # Load state dict
+    model.load_state_dict(checkpoint['model_state_dict'])
+    return model
 
 # 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_105942/topic_drift_model.pt')
+# Load topic drift detector from Hugging Face
+model = load_model()
 model.eval()
 
-# Prepare conversation window (8 turns)
+# Example conversation
 conversation = [
     "How was your weekend?",
     "It was great! Went hiking.",
@@ -145,4 +173,4 @@ print(f"Topic drift score: {drift_scores.item():.4f}")
 - Relies on BAAI/bge-m3 embeddings
 
 ## Training Curves
-![Training Curves](plots/v20241226_105942/training_curves.png)
+![Training Curves](plots/v20241226_110212/training_curves.png)