Leveraging Llama 2 for Improved Financial Sentiment Analysis: An Instruction-Tuned Approach

An instruction-tuned approach that uses Llama 2 to improve financial sentiment analysis.

An instruction-tuned approach that uses Llama 2 to improve financial sentiment analysis. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on 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 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.