Fine-Tuned BART Model for Text Summarization

Overview

This repository contains the script finetuned_bart.py, designed for fine-tuning, evaluating, and using the BART (Bidirectional and Auto-Regressive Transformers) model for text summarization. BART, developed by Facebook AI, excels in natural language understanding and generation tasks.

Features

• Fine-Tuning: Adapt the BART model to specific characteristics of a custom dataset for improved summarization.
• Summarization: Demonstrate the use of the fine-tuned model to generate summaries for new texts.

Requirements

• Python 3.9
• PyTorch
• Transformers Library (Hugging Face)
• NLTK

Dataset

https://uscode.house.gov/download/download.shtml

Usage

1. Fine-Tuning the Model:

   • Fine-tune the BART model on your dataset by running the script. Ensure your dataset is compatible with the script's data processing functions.
   • Adjust hyperparameters like batch size, learning rate, and epochs as needed.

2. Generating Summaries:

   • Generate summaries for new text inputs using the fine-tuned model.
   • The generate_summary function in the script takes a text input and outputs its summary.

Customization

Modify the script to meet specific requirements, such as changing the dataset, adjusting preprocessing steps, tweaking model parameters, or using different evaluation metrics.

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