Optimized Paragraph Summarization with a Custom Large Language Transformer Network

An optimised paragraph summarisation system built on a custom large language transformer network.

An optimised paragraph summarisation system built on a custom large language transformer network. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. The reference build uses LLM APIs, Python, Hugging Face Transformers, spaCy, and PyTorch, with a clear project structure, setup instructions and sample data so that it can be run and extended.