Fine-Tuning BART Model for Automated Text Summarization

The goal of this project is to fine-tune a BART (Bidirectional and Auto-Regressive Transformers) model to enhance automated text summarization capabilities. The fine-tuned model will be capable of generating concise and coherent summaries from long-form text documents. This project leverages the power of the transformers, datasets, and evaluate libraries to streamline the process of data handling, model training, and performance evaluation.

• News article summarization for media organizations.
• Document summarization for academic research.
• Customer feedback summarization for businesses.
• Legal document summarization for law firms.

This project aims to advance the state-of-the-art in automated text summarization, providing a valuable tool for various industries and use cases.