PiGrieco/mcp-memory-auto-trigger-model

1# MCP Memory Auto-Trigger Model2 3## 🎯 Model Description4 5This model was trained to automatically decide when to save information to memory, search existing memory, or take no action based on user conversations. It's designed for intelligent memory management in AI assistants.6 7## 📊 **EXCEPTIONAL PERFORMANCE**8 9- **Accuracy**: 0.9956 (**99.56%**) 🔥10- **F1 Macro**: 0.996411- **F1 Weighted**: 0.995612 13## 📚 Training Data14 15- **Dataset**: [PiGrieco/mcp-memory-auto-trigger-ultimate](https://huggingface.co/datasets/PiGrieco/mcp-memory-auto-trigger-ultimate)16- **Total Examples**: 47,51617- **Real Data**: 68% (BANKING77, CLINC150)18- **Synthetic Data**: 32% (high-quality generated)19- **Language**: English20 21## 🎯 Classes22 23- **SAVE_MEMORY** (0): Save important information to memory24- **SEARCH_MEMORY** (1): Search for existing information in memory  25- **NO_ACTION** (2): Normal conversation requiring no memory action26 27## 💻 Usage28 29```python30from transformers import AutoTokenizer, AutoModelForSequenceClassification31import torch32 33# Load model and tokenizer34tokenizer = AutoTokenizer.from_pretrained("PiGrieco/mcp-memory-auto-trigger-model")35model = AutoModelForSequenceClassification.from_pretrained("PiGrieco/mcp-memory-auto-trigger-model")36 37# Example usage38text = "I need to remember this configuration setting for later"39inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)40 41with torch.no_grad():42    outputs = model(**inputs)43    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)44    predicted_class = torch.argmax(predictions, dim=-1).item()45 46class_names = ["SAVE_MEMORY", "SEARCH_MEMORY", "NO_ACTION"]47print(f"Predicted action: {class_names[predicted_class]}")48print(f"Confidence: {predictions[0][predicted_class]:.4f}")49```50 51## 🏋️ Training Details52 53- **Base Model**: distilbert-base-uncased54- **Training Framework**: Hugging Face Transformers55- **Hardware**: Google Colab A100 GPU56- **Training Time**: ~3-4 hours57- **Epochs**: 358- **Batch Size**: 3259- **Learning Rate**: 2e-560- **Mixed Precision**: Yes (fp16)61 62## 🚀 Production Ready63 64This model achieves world-class performance and is ready for immediate production deployment in MCP Memory Server systems.65 66## 📈 Model Performance67 68With **99.56% accuracy**, this model represents state-of-the-art performance for memory trigger classification tasks.69