Hugging Face Evaluate Library 101 Master Llm Testing

Large language models (LLMs) now power everything from chatbots to content generation tools – but how do we separate hype from reality when evaluating their performance? Robust evaluation frameworks are critical, yet often overlooked in the rush to adopt AI. Let’s cut through the abstraction and give you concrete methods to assess whether an LLM truly meets your project’s needs. Evaluating LLMs isn’t just a technical exercise—it’s about ensuring your models deliver value. Whether you’re building a summarization tool or a question-answering system, you need reliable ways to measure performance. Studies show that poorly evaluated models can lead to a 20-30% drop in user satisfaction due to inaccurate outputs.

That’s a big deal for businesses and developers alike. Hugging Face Evaluate library steps in as a practical solution, offering dozens of metrics to test your models across tasks like text summarization, translation, and classification. It’s open-source, easy to use, and packed with features that save time and boost accuracy. 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. If you've ever wondered how to make sure an LLM performs well on your specific task, this guide is for you!

It covers the different ways you can evaluate a model, guides on designing your own evaluations, and tips and tricks from practical experience. Whether working with production models, a researcher or a hobbyist, I hope you'll find what you need; and if not, open an issue (to suggest ameliorations or missing resources) and I'll complete the guide! In text, links prefixed by ⭐ are links I really enjoyed and recommend reading. If you want an intro on the topic, you can read this blog on how and why we do evaluation! The most densely practical part of this guide. I’ve recently been speaking with various teams building LLM-based applications, and a recurring theme has been the challenge of effectively testing these systems.

While I’m aware of several specialized frameworks for this purpose, my goal is to demonstrate how existing, familiar testing frameworks can be adapted for robust LLM evaluation. Developing applications powered by Large Language Models (LLMs) presents unique testing challenges. Unlike traditional software, LLMs are non-deterministic, and their “correct” output can be subjective. How do you ensure your LLM-driven features consistently deliver high-quality, relevant, and accurate responses? This article will explore a practical approach to testing LLM applications, specifically focusing on how to integrate quantitative evaluation metrics using the Hugging Face evaluate library within structured testing frameworks like Robot Framework and... Traditional testing methodologies often fall short when it comes to LLMs:

To address these, we need a way to measure the quality of LLM responses objectively and automatically. 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. The LLM Evaluation Guidebook provides comprehensive guidance on how to evaluate Large Language Models (LLMs) across different contexts and requirements. This page introduces the purpose, structure, and key components of the guidebook, serving as a starting point for understanding the available evaluation approaches and how to use them effectively. This guidebook helps you ensure that your LLM performs well on specific tasks by explaining:

Whether you're working with production models, conducting academic research, or experimenting as a hobbyist, the guidebook offers relevant guidance tailored to your needs. The guidebook is organized around three core evaluation approaches, with supporting content for implementation and troubleshooting: The guidebook covers three primary evaluation methodologies, each with distinct characteristics and use cases: 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. The rise of generative AI and LLMs like GPT-4, Llama or Claude enables a new era of AI drive applications and use cases. However, evaluating these models remains an open challenge. Academic benchmarks can no longer always be applied to generative models since the correct or most helpful answer can be formulated in different ways, which would give limited insight into real-world performance.

So, how can we evaluate the performance of LLMs if previous methods are not long valid? Two main approaches show promising results for evaluating LLMs: leveraging human evaluations and using LLMs themselves as judges. Human evaluation provides the most natural measure of quality but does not scale well. Crowdsourcing services can be used to collect human assessments on dimensions like relevance, fluency, and harmfulness. However, this process is relatively slow and costly. Recent research has proposed using LLMs themselves as judges to evaluate other LLMs, an approach called LLM-as-a-judge demonstrates that large LLMs like GPT-4 can match human preferences with over 80% agreement when evaluating conversational...

and get access to the augmented documentation experience With a finetuned model through either SFT or LoRA SFT, we should evaluate it on standard benchmarks. As machine learning engineers you should maintain a suite of relevant evaluations for your targeted domain of interest. In this page, we will look at some of the most common benchmarks and how to use them to evaluate your model. We’ll also look at how to create custom benchmarks for your specific use case. Automatic benchmarks serve as standardized tools for evaluating language models across different tasks and capabilities.

While they provide a useful starting point for understanding model performance, it’s important to recognize that they represent only one piece of a comprehensive evaluation strategy. Automatic benchmarks typically consist of curated datasets with predefined tasks and evaluation metrics. These benchmarks aim to assess various aspects of model capability, from basic language understanding to complex reasoning. The key advantage of using automatic benchmarks is their standardization - they allow for consistent comparison across different models and provide reproducible results. However, it’s crucial to understand that benchmark performance doesn’t always translate directly to real-world effectiveness. A model that excels at academic benchmarks may still struggle with specific domain applications or practical use cases.

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