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https://github.com/huggingface/evaluate
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
https://github.com/huggingface/evaluate
evaluation machine-learning
Last synced: 20 days ago
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🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
- Host: GitHub
- URL: https://github.com/huggingface/evaluate
- Owner: huggingface
- License: apache-2.0
- Created: 2022-03-30T15:08:26.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-18T20:43:45.000Z (8 months ago)
- Last Synced: 2024-03-24T16:33:09.425Z (7 months ago)
- Topics: evaluation, machine-learning
- Language: Python
- Homepage: https://huggingface.co/docs/evaluate
- Size: 1.97 MB
- Stars: 1,762
- Watchers: 50
- Forks: 215
- Open Issues: 168
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Authors: AUTHORS
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README
> **Tip:** For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library [LightEval](https://github.com/huggingface/lighteval).
🤗 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.
[🎓 **Documentation**](https://huggingface.co/docs/evaluate/)
🔎 **Find a [metric](https://huggingface.co/evaluate-metric), [comparison](https://huggingface.co/evaluate-comparison), [measurement](https://huggingface.co/evaluate-measurement) on the Hub**
[🌟 **Add a new evaluation module**](https://huggingface.co/docs/evaluate/)
🤗 Evaluate also has lots of useful features like:
- **Type checking**: the input types are checked to make sure that you are using the right input formats for each metric
- **Metric cards**: each metrics comes with a card that describes the values, limitations and their ranges, as well as providing examples of their usage and usefulness.
- **Community metrics:** Metrics live on the Hugging Face Hub and you can easily add your own metrics for your project or to collaborate with others.
# Installation
## With pip
🤗 Evaluate can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance)
```bash
pip install evaluate
```
# Usage
🤗 Evaluate's main methods are:
- `evaluate.list_evaluation_modules()` to list the available metrics, comparisons and measurements
- `evaluate.load(module_name, **kwargs)` to instantiate an evaluation module
- `results = module.compute(*kwargs)` to compute the result of an evaluation module
# Adding a new evaluation module
First install the necessary dependencies to create a new metric with the following command:
```bash
pip install evaluate[template]
```
Then you can get started with the following command which will create a new folder for your metric and display the necessary steps:
```bash
evaluate-cli create "Awesome Metric"
```
See this [step-by-step guide](https://huggingface.co/docs/evaluate/creating_and_sharing) in the documentation for detailed instructions.
## Credits
Thanks to [@marella](https://github.com/marella) for letting us use the `evaluate` namespace on PyPi previously used by his [library](https://github.com/marella/evaluate).