Project Overview

An end-to-end NLP text summarization pipeline that transforms lengthy documents into concise, meaningful summaries. This project leverages transformer-based models to extract key information while preserving the core message of the original text.

Built with a modular architecture following MLOps best practices, the project includes data ingestion, validation, transformation, model training, and evaluation stages for a complete machine learning workflow.

Key Features

• End-to-end text summarization pipeline
• Transformer-based model architecture
• Modular code structure with config management
• Data validation and transformation pipelines
• Model training and evaluation components