Releases Huggingface Evaluate Github

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There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. Tip: For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library LightEval. 🤗 Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.

🔎 Find a metric, comparison, measurement on the Hub 🤗 Evaluate also has lots of useful features like: 🤗 Evaluate can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance) There was an error while loading. Please reload this page. There was an error while loading.

Please reload this page. We encountered the following issues today with pip3 install evaluate==0.4.5 rouge_score==0.1.2. The issue is This issue can be resolved after we fix huggingface-hub==0.36.0. It is due to https://github.com/huggingface/evaluate/blob/v0.4.5/setup.py#L54 does not check major version < 1.0.0 and pip uses latest available version. This affects previous stable versions.

Could you enable stricter dependency checks? and get access to the augmented documentation experience You can evaluate AI models on the Hub in multiple ways and this page will guide you through the different options: Community leaderboards show how a model performs on a given task or domain. For example, there are leaderboards for question answering, reasoning, classification, vision, and audio. If you’re tackling a new task, you can use a leaderboard to see how a model performs on it.

Here are some examples of community leaderboards: There are many more leaderboards on the Hub. Check out all the leaderboards via this search or use this dedicated Space to find a leaderboard for your task. and get access to the augmented documentation experience You can evaluate AI models on the Hub in multiple ways and this page will guide you through the different options: Community leaderboards show how a model performs on a given task or domain.

For example, there are leaderboards for question answering, reasoning, classification, vision, and audio. If you’re tackling a new task, you can use a leaderboard to see how a model performs on it. Here are some examples of community leaderboards: There are many more leaderboards on the Hub. Check out all the leaderboards via this search or use this dedicated Space to find a leaderboard for your task. There was an error while loading.

Please reload this page. There was an error while loading. Please reload this page. and get access to the augmented documentation experience Before you start, you will need to setup your environment and install the appropriate packages. 🤗 Evaluate is tested on Python 3.7+.

You should install 🤗 Evaluate in a virtual environment to keep everything neat and tidy. Create and navigate to your project directory: Start a virtual environment inside the directory: pip install evaluate Copy PIP instructions HuggingFace community-driven open-source library of evaluation Tip: For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library LightEval.

🤗 Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized. 🔎 Find a metric, comparison, measurement on the Hub and get access to the augmented documentation experience 🤗 Evaluate provides access to a wide range of evaluation tools. It covers a range of modalities such as text, computer vision, audio, etc. as well as tools to evaluate models or datasets.

These tools are split into three categories. There are different aspects of a typical machine learning pipeline that can be evaluated and for each aspect 🤗 Evaluate provides a tool: Each of these evaluation modules live on Hugging Face Hub as a Space. They come with an interactive widget and a documentation card documenting its use and limitations. For example accuracy: Each metric, comparison, and measurement is a separate Python module, but for using any of them, there is a single entry point: evaluate.load()!

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