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NLP Document Classifier

NLP Document Classifier

PythonBERTscikit-learnDocker
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Project Overview

A sophisticated document classification system powered by BERT and scikit-learn, capable of processing and categorizing over 1 million documents with 92% accuracy. The system handles multiple languages and document formats while maintaining high performance.

Key Features

Multi-language Support

Processes documents in 95+ languages using multilingual BERT models with consistent accuracy across languages.

Efficient Processing

Handles various document formats (PDF, DOC, TXT) with distributed processing for high throughput.

Real-time Classification

Provides instant document classification with confidence scores and category explanations.

Security Features

Implements document encryption, access controls, and audit logging for sensitive content.

Technical Details

  • NLP Stack: BERT (base-multilingual), Transformers library, and custom fine-tuning pipeline
  • Processing: Distributed processing with Celery, Redis for caching, and MongoDB for document storage
  • API: FastAPI backend with async processing and WebSocket support for real-time updates
  • Deployment: Containerized with Docker and orchestrated using Kubernetes for scalability