Evaluate Hugging Face

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. 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) The Hugging Face evaluate library provides a simple and flexible interface for computing metrics on machine learning predictions. It’s especially well-suited for NLP tasks like classification, summarization, and translation, where standard metrics are critical for reliable benchmarking. Measures token overlap with a reference: * rouge-1: unigram overlap * rouge-2: bigram overlap Precision-based n-gram overlap metric, often used in translation: Uses a pretrained transformer model to measure semantic similarity in embedding space.

There’s a small bug here, I don’t have time to fix it - however similar code should work. The evaluate library is model-agnostic and pairs well with the rest of the Hugging Face ecosystem. It’s lightweight enough for quick experiments but supports rich comparisons for production or publication. and get access to the augmented documentation experience A library for easily evaluating machine learning models and datasets. With a single line of code, you get access to dozens of evaluation methods for different domains (NLP, Computer Vision, Reinforcement Learning, and more!).

Be it on your local machine or in a distributed training setup, you can evaluate your models in a consistent and reproducible way! Visit the 🤗 Evaluate organization for a full list of available metrics. Each metric has a dedicated Space with an interactive demo for how to use the metric, and a documentation card detailing the metrics limitations and usage. Tip: For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library LightEval. This page provides a comprehensive guide on how to create, implement, and share custom metrics for the Hugging Face Evaluate library. Custom metrics allow you to extend the evaluation capabilities of the library beyond the built-in metrics to address specific evaluation needs for your machine learning models.

For information about using existing metrics, see Metrics. For details on the overall evaluation module architecture, see Evaluation Module Base Classes. Custom metrics in the Evaluate library follow the same structure as built-in metrics, inheriting from the EvaluationModule base class. The process of creating a custom metric involves: Sources: measurements/label_distribution/label_distribution.py measurements/label_distribution/README.md A custom metric module consists of several files organized in a specific structure:

There was an error while loading. Please reload this page. This guide will show how to load a pre-trained Hugging Face pipeline, log it to MLflow, and use mlflow.evaluate() to evaluate builtin metrics as well as custom LLM-judged metrics for the model. For detailed information, please read the documentation on using MLflow evaluate. Here we are loading a text generation pipeline, but you can also use either a text summarization or question answering pipeline. We log our pipeline as an MLflow Model, which follows a standard format that lets you save a model in different "flavors" that can be understood by different downstream tools.

In this case, the model is of the transformers "flavor". Load in a dataset from Hugging Face Hub to use for evaluation. Research Methodology: This analysis is based on official Hugging Face documentation, GitHub repository inspection, and verified public API references. All capabilities and metrics are sourced from official documentation. What it is: Hugging Face Evaluate is a library providing standardized evaluation methods for machine learning models across NLP, Computer Vision, and Reinforcement Learning domains with "dozens of popular metrics" accessible via simple API. Key capabilities (Verified from Documentation):

Implementation effort: Medium complexity (2-3 person-weeks) due to metric selection and integration requirements. Status: RECOMMEND - Production-ready with strong ecosystem integration, though newer LightEval recommended for LLM-specific evaluation. and get access to the augmented documentation experience The goal of the 🤗 Evaluate library is to support different types of evaluation, depending on different goals, datasets and models. Here are the types of evaluations that are currently supported with a few examples for each: A metric measures the performance of a model on a given dataset.

This is often based on an existing ground truth (i.e. a set of references), but there are also referenceless metrics which allow evaluating generated text by leveraging a pretrained model such as GPT-2. Metrics are often used to track model performance on benchmark datasets, and to report progress on tasks such as machine translation and image classification.

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