Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.
It currently contains:
- implementations of dozens of popular metrics: the existing metrics cover a variety of tasks spanning from NLP to Computer Vision, and include dataset-specific metrics for datasets. With a simple command like
accuracy = load("accuracy"), get any of these metrics ready to use for evaluating a ML model in any framework (Numpy/Pandas/PyTorch/TensorFlow/JAX).
- comparisons and measurements: comparisons are used to measure the difference between models and measurements are tools to evaluate datasets.
- an easy way of adding new evaluation modules to the 🤗 Hub: you can create new evaluation modules and push them to a dedicated Space in the 🤗 Hub with evaluate-cli create [metric name], which allows you to see easily compare different metrics and their outputs for the same sets of references and predictions.
