Practical overview
When it fits
Use Evaluate when a Python machine-learning workflow needs reusable evaluation modules for metrics, model comparisons or dataset measurements, rather than a hosted dashboard.
Who it is for
- Machine-learning developers who need standardized, reusable model metrics.
- Teams comparing model outputs against references or assessing dataset properties.
- Developers creating and sharing custom evaluation modules through the Hugging Face Hub.
Documented uses
- List and load available metrics, comparisons or measurements.
- Compute evaluation results for model outputs using a selected module.
- Compare models and measure dataset properties.
- Create a custom evaluation module and share it through the Hugging Face Hub.
Requirements & limits
The library is open source under Apache-2.0. Installation is documented with pip and requires a virtual environment such as venv or conda.
- Evaluation modules have their own input formats and arguments; the library provides input type checking.
- Metric cards describe each metric’s values, limitations and ranges.
- The project README recommends LightEval for newer evaluation approaches, including some LLM evaluation use cases.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Compute a result with an evaluation module
Install Evaluate, find an evaluation module, load it and compute its result.
Before you start
- Python environment using venv or conda
- Python package installation with pip
- Predictions or other inputs in the format required by the chosen module
Steps
Install the library with the command below.
pip install evaluateUse
evaluate.list_evaluation_modules()to list available metrics, comparisons and measurements.Load a module with
evaluate.load(module_name, **kwargs).Call
module.compute(*kwargs)with the inputs required by that module.
Expected outcome
A computed result from the selected evaluation module.
Guide limitations
- Input formats and required arguments depend on the selected module; metric cards describe values, limitations and ranges.
- The README recommends LightEval for more recent approaches such as some LLM evaluation use cases.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- huggingface/evaluate · Official homepage title · retained in its source language