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Feature/dual classifier yossio (#11)
* Increate tests connection timeout from 30 to 60 * feat: Add dual classifier implementation - Added dual classifier module with training and testing capabilities * docs: Add README for trained_model directory and improve gitignore * small readme update. --------- Co-authored-by: yovadia <[email protected]>
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‎.gitignore

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# Project specific
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bin/
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# Model files (too large for git)
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*.pt
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*.pth
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*.bin
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*.onnx
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*.h5
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*/trained_model/*.pt
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*/trained_model/*.pth
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*/trained_model/*.bin
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*/trained_model/*.onnx
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*/trained_model/*.h5
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*/trained_model/*.json
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*/trained_model/*.txt
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*/models/*.pt
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*/models/*.pth
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*/models/*.bin
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*/models/*.onnx
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*/models/*.h5
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*/models/*.json
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*/models/*.txt
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# Allow README files in model directories
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!*/trained_model/README.md
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!*/models/README.md
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# Added by Claude Task Master
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# Logs
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logs
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*.log
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npm-debug.log*
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yarn-debug.log*
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yarn-error.log*
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dev-debug.log
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node_modules/
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# Editor directories and files
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.idea
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.vscode
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*.suo
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*.ntvs*
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*.njsproj
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*.sln
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*.sw?
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# Task files
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tasks.json
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tasks/
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.cursor/
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.roo/
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.env.example
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.taskmasterconfig
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example_prd.txt
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.roomodes
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.windsurfrules
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scripts/prd.txt
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.env.taskmaster
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package-lock.json
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package.json

‎dual_classifier/DUAL_CLASSIFIER_SYSTEM_TEST_SUMMARY.md

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# Task 2 Testing Summary: Dual-Head Architecture POC with Training
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## Overview
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Task 2 successfully implemented and tested a complete dual-purpose DistilBERT classifier with comprehensive training infrastructure for both category classification and PII detection using a shared model architecture.
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## Test Coverage
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### ✅ Component Tests (14/14 Passed)
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#### 1. Synthetic Data Generator Tests
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- **Initialization**: Validates proper setup of 10 categories, templates, and 5 PII pattern types
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- **Sample Generation**: Tests both PII and non-PII sample creation with proper labeling
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- **Dataset Generation**: Validates batch dataset creation with configurable PII ratios
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- **PII Pattern Detection**: Confirms email and phone number detection in text
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#### 2. Dual-Task Dataset Tests
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- **Dataset Creation**: Validates PyTorch Dataset implementation with correct tensor shapes
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- **Tokenization**: Tests DistilBERT tokenizer integration with proper padding/truncation
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- **Label Alignment**: Ensures category and PII labels align with tokenized sequences
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#### 3. Dual-Task Loss Function Tests
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- **Loss Initialization**: Validates weighted loss combining category and PII objectives
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- **Loss Computation**: Tests gradient flow and loss calculation for both tasks
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- **Padding Mask Handling**: Ensures padded tokens are properly ignored in PII loss
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#### 4. Dual-Task Trainer Tests
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- **Trainer Initialization**: Validates setup with proper data loaders and optimizers
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- **Training Step**: Confirms model parameters update during training
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- **Evaluation**: Tests validation metrics calculation (accuracy, F1-score)
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- **Model Persistence**: Validates save/load functionality with state preservation
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#### 5. Integration Tests
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- **End-to-End Training**: Complete training pipeline with 2 epochs
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- **Memory Efficiency**: Confirms dual-head architecture has reasonable parameter count (~67M)
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## Performance Results
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### Training Performance
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- **Dataset Size**: 50 training samples, 20 validation samples
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- **Training Time**: 18.6 seconds (0.372 seconds per sample)
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- **Performance Rating**: 🚀 Excellent performance!
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- **System**: 8-core CPU, 16GB RAM (no GPU required)
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### Model Architecture
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- **Base Model**: DistilBERT (66M parameters)
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- **Total Parameters**: 67,553,292 (efficient shared backbone)
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- **Category Head**: 10-class classification
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- **PII Head**: Token-level binary classification
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### Training Results (From Previous Run)
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- **Final Training Metrics**:
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- Training Loss: 1.4948
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- Category Loss: 1.3069
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- PII Loss: 0.1879
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- **Final Validation Metrics**:
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- Validation Loss: 1.5169
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- Category Accuracy: 45%
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- PII F1-Score: 91.09%
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## Test Infrastructure
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### Automated Testing
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```bash
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# Run full test suite
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python -m pytest test_dual_classifier_system.py -v
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# Run with performance test
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python test_dual_classifier_system.py
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```
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### Manual Validation
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```bash
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# Test existing trained model
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python test_existing_model.py
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```
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## Key Technical Achievements
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### 1. **Multi-Task Learning Architecture**
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- Single DistilBERT backbone serving dual purposes
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- Separate classification heads for different tasks
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- Shared representations for memory efficiency
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### 2. **Robust Training Pipeline**
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- Combined loss function with task weighting
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- Proper gradient flow and parameter updates
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- Validation metrics for both tasks
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### 3. **Synthetic Data Generation**
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- 10 category templates (math, science, history, etc.)
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- 5 PII pattern types (email, phone, SSN, name, address)
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- Configurable PII injection rates
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- Token-level PII labeling
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### 4. **Production-Ready Features**
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- Model persistence (save/load)
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- Training history tracking
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- Progress monitoring with tqdm
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- Memory-efficient data loading
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## Testing Methodology
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### Unit Tests
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- Individual component validation
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- Mock data for isolated testing
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- Edge case handling
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### Integration Tests
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- Full pipeline validation
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- Real data flow testing
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- Performance benchmarking
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### Validation Tests
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- Model loading/saving
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- Prediction consistency
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- Memory efficiency
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## File Structure
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```
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dual_classifier/
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├── test_dual_classifier_system.py # Comprehensive test suite
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├── test_existing_model.py # Trained model validation
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├── DUAL_CLASSIFIER_SYSTEM_TEST_SUMMARY.md # This summary
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├── dual_classifier.py # Core model implementation
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├── trainer.py # Training infrastructure
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├── data_generator.py # Synthetic data generation
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├── train_example.py # Training demonstration
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└── trained_model/ # Saved model artifacts
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```
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## Success Criteria Met
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✅ **Dual-Purpose Architecture**: Single model for both category and PII classification
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✅ **Memory Optimization**: Shared backbone reduces total parameters vs. separate models
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✅ **Training Infrastructure**: Complete pipeline with loss functions and metrics
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✅ **Data Generation**: Synthetic dataset with realistic PII patterns
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✅ **Model Persistence**: Save/load functionality with state preservation
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✅ **Performance Validation**: Acceptable training speed on laptop hardware
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✅ **Test Coverage**: Comprehensive test suite with 14 passing tests
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## Next Steps
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Task 2 is fully complete and validated. The implementation provides a solid foundation for:
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- Task 3: Data Pipeline Implementation (real dataset integration)
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- Task 4: Advanced Training Pipeline (optimization and scaling)
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- Task 5: Rust Implementation with Candle (performance optimization)
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## Performance Notes
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- Training completes in under 20 seconds for 50 samples
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- Model achieves 45% category accuracy and 91% PII F1-score on small synthetic dataset
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- Memory usage is efficient for laptop deployment
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- No GPU required for development and testing

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