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README.md

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+# Query Classifier Model
+
+This is a probabilistic multi-label classifier for routing business queries to appropriate systems.
+
+## Model Description
+- **Architecture**: Multi-layer neural network with sigmoid outputs
+- **Input**: TF-IDF vectorized queries (max 500 features)  
+- **Output**: Independent probabilities for each system
+- **Systems**: accounting, communication, enterprise_resource_planning, knowledge_management, project_management, sales
+
+## Performance
+- **Macro F1**: 0.817
+- **Micro F1**: 0.803
+- **Subset Accuracy**: 0.704
+- **Hamming Loss**: 0.071
+
+## Usage
+
+```python
+from src.core.services.query_classifier_service import classify_query
+
+# Classify a single query
+result = classify_query("show total revenue for last quarter")
+print(f"Top system: {result['top_system']}")
+print(f"Confidence: {result['confidence']:.3f}")
+print(f"All systems: {result['predicted_systems']}")
+```
+
+## Training Data
+- **Total queries**: 621
+- **Training samples**: 496
+- **Test samples**: 125
+- **Sources**: Apache Calcite fine-tuning data (Jira, QuickBooks, HubSpot, Gmail, Notion, SAP)
+
+## Systems Supported
+- **accounting**: Accounting
+- **communication**: Communication
+- **enterprise_resource_planning**: Enterprise Resource Planning
+- **knowledge_management**: Knowledge Management
+- **project_management**: Project Management
+- **sales**: Sales
+
+## Model Files
+- `final_model.pth`: PyTorch model weights and configuration
+- `vectorizer.pkl`: TF-IDF vectorizer for text preprocessing
+- `mlb.pkl`: Multi-label binarizer for label encoding
+- `metadata.json`: Model metadata and performance metrics