The "Text Summarization with Hugging Face" project aims to build an automated system that can generate concise summaries from large bodies of text using state-of-the-art natural language processing models. Leveraging the Hugging Face library, this project integrates pre-trained transformer models like BART and T5 to extract key points and present a coherent summary. It involves data preprocessing, model fine-tuning, and evaluation of generated outputs against human-written summaries to ensure quality and accuracy. This project highlights the practical application of machine learning in content simplification, making it ideal for research, news, and academic use cases.
MohammadShabazuddin/Text_Summarizer_project
Implemented a text summarization project using Hugging Face transformers to generate concise summaries of large documents efficiently.
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