evalsense 0.1.2

Tools for evaluating large language models.

[!WARNING] This project is a work in progress. Critical components may be missing, inoperative or incomplete, and the API can undergo major changes without any notice. Please check back later for a more stable version.

EvalSense: LLM Evaluation

About

This repository holds a Python package enabling systematic evaluation of large language models (LLMs) on open-ended generation tasks, with a particular focus on healthcare and summarisation. It also includes supplementary documentation and assets related to the NHS England project on LLM evaluation, such as the code for an interactive LLM evaluation guide (located in the guide/ directory). You can find more information about the project in the original project proposal.

Note: Only public or fake data are shared in this repository.

Project Stucture

• The main code for the EvalSense Python package can be found under evalsense/.
• The accompanying documentation is available in the docs/ folder.
• Code for the interactive LLM evaluation guide is located under guide/.
• Jupyter notebooks with the evaluation experiments and examples are located under notebooks/.

Getting Started

Installation for Development

To install the project for local development, you can follow the steps below:

To clone the repo:

git clone [e-mail]:nhsengland/evalsense.git

To setup the Python environment for the project:

• Install uv if it's not installed already
• uv sync --all-extras
• source .venv/bin/activate
• pre-commit install

To setup the Node environment for the LLM evaluation guide (located under guide/):

• Install node if it's not installed already
• npm install in the guide/ directory
• npm run start to run the development server

Usage

For an example illustrating the usage of EvalSense, please check the Demo notebook under the notebooks/ folder.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/amazing-feature)
3. Commit your Changes (git commit -m 'Add some amazing feature')
4. Push to the Branch (git push origin feature/amazing-feature)
5. Open a Pull Request

See CONTRIBUTING.md for detailed guidance.

License

Unless stated otherwise, the codebase is released under the MIT Licence. This covers both the codebase and any sample code in the documentation.

See LICENSE for more information.

The documentation is © Crown copyright and available under the terms of the Open Government 3.0 licence.

Contact

To find out more about the NHS England Data Science visit our project website or get in touch at [e-mail].