evaluate

Why we included this project

Evaluating a model is often the messiest part of an ML project, and this library takes most of the awkwardness out of it. Instead of hand-rolling your own scorer, you load one of the dozens of built-in metrics spanning NLP, computer vision, and dataset-specific tasks with a single call like `load("accuracy")`, and the same implementation works whether your outputs come from NumPy, Pandas, PyTorch, TensorFlow, or JAX. It also includes comparisons for measuring differences between models and measurements for checking datasets, so you can report more than one number at a time. Metrics are versioned on the Hub with dedicated cards, which makes it easy to see a metric's ranges and limitations and to share or publish custom metrics for a team or the community. If most of your work is LLM benchmarking, the maintainers now point to LightEval instead, but for standard metric evaluation across frameworks Evaluate is a solid, well-documented choice.