Training Custom Models
Introduction
Custom AI models allow you to tailor document classification to your specific needs and improve accuracy for specialized document types.
Model Types
Classification Models
- Document type classification
- Privilege classification
- Relevance scoring
- Custom category assignment
Extraction Models
- Entity extraction
- Key-value pair identification
- Table data extraction
- Metadata extraction
Training Process
Data Preparation
- Dataset Collection: Gather representative training documents
- Data Annotation: Label documents with correct classifications
- Quality Control: Verify annotation accuracy
- Data Splitting: Create training, validation, and test sets
Model Training
- Feature Engineering: Extract relevant document features
- Algorithm Selection: Choose appropriate ML algorithms
- Hyperparameter Tuning: Optimize model parameters
- Cross-Validation: Validate model performance
Model Evaluation
- Accuracy metrics
- Precision and recall analysis
- Confusion matrix review
- Performance benchmarking
Best Practices
- Use diverse training data
- Implement proper validation techniques
- Regular model retraining
- Monitor performance in production
- Document training procedures
Deployment and Monitoring
- Model versioning
- A/B testing
- Performance monitoring
- Feedback collection
- Continuous improvement