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',_,c,$,m,S="A library for easily evaluating machine learning models and datasets.",E,p,z="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!",L,g,A='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.',T,f,O='

Tip: For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library LightEval.

',C,u,R='
Tutorials

Learn the basics and become familiar with loading, computing, and saving with 🤗 Evaluate. Start here if you are using 🤗 Evaluate for the first time!

How-to guides

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.

Conceptual guides

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.

Reference

Technical descriptions of how 🤗 Evaluate classes and methods work.

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