NLP System for Document Classification
Designed and deployed a multi-label document classification system processing 1M+ documents daily. Implemented active learning to continuously improve model performance.
Technologies Used
Project Overview
Built a scalable NLP system for multi-label document classification handling enterprise-scale document processing. The system uses advanced transformer models with custom fine-tuning for domain-specific classification tasks.
Key Features: ā¢ Multi-label classification with hierarchical labels ā¢ Active learning pipeline for continuous improvement ā¢ Real-time processing with sub-second latency ā¢ Scalable microservices architecture ā¢ Comprehensive monitoring and alerting
The system processes various document types including contracts, invoices, reports, and correspondence. It supports over 50 document categories with hierarchical classification structure.
Key Challenges
- Handling diverse document formats and layouts
- Scaling to 1M+ documents per day
- Maintaining accuracy across 50+ categories
Impact & Results
Automated document processing for enterprise clients, reducing manual classification time by 90% and improving accuracy by 25%.