CHEESE: A Co-adaptive Harness for Effective Evaluation, Steering, and Enhancement
CarperAI, an AI startup has announced the release of CHEESE, a powerful new tool for collecting human feedback data. CHEESE is designed to make it easy for researchers and developers to collect feedback on their machine learning models, using a simple API that can turn any Gradio experiment into a feedback collection platform.
With the v0.1 release of CHEESE, they are providing an API for collecting feedback from a Gradio demo, along with numerous examples of how to use it for different tasks. These include text completion reranking, image selection, and design feedback. We have also included detailed documentation on how to use CHEESE for your own projects, making it easy for anyone to get started.
One of the key features of CHEESE is its co-adaptive design, which allows it to adapt to the specific needs of each task. This makes it an ideal tool for researchers and developers who are working on a wide range of projects, from natural language processing to computer vision.
In addition to its flexibility, CHEESE is also designed to be user-friendly and easy to use. The API is simple and intuitive, making it easy to integrate into existing projects and workflows. And the documentation is clear and detailed, so you can get up and running quickly.
CHEESE will be an invaluable tool for anyone working on machine learning projects. Whether you're a researcher, developer, or data scientist, it can help you collect the feedback you need to improve your models and make them more effective.
PS: Above blog is mostly generated by ChatGPT