Metadata-Version: 2.1
Name: evaluate
Version: 0.4.1
Summary: HuggingFace community-driven open-source library of evaluation
Home-page: https://github.com/huggingface/evaluate
Author: HuggingFace Inc.
Author-email: leandro@huggingface.co
License: Apache 2.0
Download-URL: https://github.com/huggingface/evaluate/tags
Keywords: metrics machine learning evaluate evaluation
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
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<p align="center">
    <br>
    <img src="https://huggingface.co/datasets/evaluate/media/resolve/main/evaluate-banner.png" width="400"/>
    <br>
</p>

<p align="center">
    <a href="https://github.com/huggingface/evaluate/actions/workflows/ci.yml?query=branch%3Amain">
        <img alt="Build" src="https://github.com/huggingface/evaluate/actions/workflows/ci.yml/badge.svg?branch=main">
    </a>
    <a href="https://github.com/huggingface/evaluate/blob/master/LICENSE">
        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/evaluate.svg?color=blue">
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    </a>
    <a href="https://github.com/huggingface/evaluate/releases">
        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/evaluate.svg">
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        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg">
    </a>
</p>

🤗 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).