🤗 Evaluate
A library for easily evaluating machine learning models and datasets.
With a single line of code, you get access to dozens of evaluation methods for different domains (NLP, Computer Vision, Reinforcement Learning, and more!). Be it on your local machine or in a distributed training setup, you can evaluate your models in a consistent and reproducible way!
Visit the 🤗 Evaluate organization for a full list of available metrics. Each metric has a dedicated Space with an interactive demo for how to use the metric, and a documentation card detailing the metrics limitations and usage.
Tip: For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library LightEval.
Learn the basics and become familiar with loading, computing, and saving with 🤗 Evaluate. Start here if you are using 🤗 Evaluate for the first time!
Practical guides to help you achieve a specific goal. Take a look at these guides to learn how to use 🤗 Evaluate to solve real-world problems.
High-level explanations for building a better understanding of important topics such as considerations going into evaluating a model or dataset and the difference between metrics, measurements, and comparisons.
Technical descriptions of how 🤗 Evaluate classes and methods work.