Hugging Face Evaluate Released
Hugging Face has released a new library for evaluating models and datasets, called Hugging Face Evaluate. It comes with built-in tools for dataset and model evaluation, comparison, and measurement.
Created on June 2|Last edited on June 2
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The team at Hugging Face have released a new useful library for evaluating machine learning models, simply named Hugging Face Evaluate (🤗 Evaluate).
Hugging Face Evaluate has a variety of method built-in for evaluating all kinds of models, including NLP, computer vision, reinforcement learning, and more. Not only can it evaluate model metrics, but it also has tools for evaluating the datasets they run on. Hugging Face Evaluate can be used for comparisons between models, model information storing, and is even customizable with support for custom-made metrics.
What can Hugging Face Evaluate be used for?
There are three main pieces to Hugging Face Evaluate: Metrics, comparisons, and measurements.
Metrics measure how a model performs on a given dataset, something that's applicable to any kind of model. Things like prediction accuracy, performance on text generation, and more. There are a number of built-in metrics for a wide range of use cases, but custom metrics are also able to be built and used for more specific requirements.
Comparisons are used when comparing two or more models on test datasets. This can help you see what's working and what's not, helping you decide the best path forward for future development.
Measurements are tools that help you gain insights on your datasets and models.
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