Smart Summarizer: BERT-Enhanced Text Summarization with Training Visualization

Overview

This project demonstrates the use of BERT for masked language modeling and text summarization, integrating both Natural Language Processing (NLP) and Machine Learning (ML). It includes the steps for data preprocessing, model training, and visualization of training results. The project is designed to provide an in-depth understanding of the training process and the effectiveness of the model through detailed loss plots.

Features

• BERT-Based Model: Utilizes BERT for masked language modeling to enhance text summarization capabilities.
• Data Preprocessing: Efficient data handling and preprocessing steps to prepare datasets for training.
• Training Visualization: Detailed plots of training and validation losses to visualize model performance over epochs.
• Google Drive Integration: Easy data storage and retrieval using Google Drive for seamless data handling.

Prerequisites

• Python 3.6 or higher
• PyTorch
• Hugging Face Transformers
• Datasets
• Matplotlib
• Google Colab (optional for running the notebook)

Installation

Clone the repository:

git clone https://github.com/yourusername/smart-summarizer-bert.git
cd smart-summarizer-bert