Welcome to the End-to-End Text Summarizer repository! This project aims to develop a sophisticated system for generating concise and meaningful text summaries using advanced natural language processing and machine learning techniques.
- Pegasus Model: Utilizes the state-of-the-art Pegasus model for effective abstractive text summarization.
- Modular Coding: Implements a modularized coding structure for enhanced organization and maintainability.
- Trainer Class: Leverages the Trainer class from the Hugging Face transformers library for streamlined model training.
- CI/CD Pipeline: Integrates continuous integration and continuous deployment (CI/CD) pipelines for seamless development practices.
- Web Application: Expands functionality with a user-friendly web application powered by FastAPI for real-time text summarization predictions.
- Python
- Knowledge in NLP
- Model training
git clone https://github.com/Jerrinthomas007/End-To-End-NLP-Project---Text-Summarization
cd End-To-End-NLP-Project---Text-Summarization
pip install -r requirements.
python app.py
How open the chrome and run the " localhost:8080 ". You can train the model from the app itself and also predict after using the app after the successfull completion of training
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Training the model by increasing the no. of epochs can give better and more accurate results.
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Using GPU to train the model will be better where cpu takes lot of time train the model