4_evaluation

Evaluation

This module covers evaluation approaches for your smol model, including both standard benchmarks and domain-specific evaluation methods.

In this module we will use the library lighteval. It's made at Hugging Face and it's integrated with the Hugging Face ecosystem. If you want to go deeper into the topic of evaluation with the authors of lighteval, you can check the evaluation guidebook.

Module Overview

Evaluating language models focuses on assessing core capabilities:

• Task Performance: How well the model performs on specific tasks like question answering, summarization, etc.
• Output Quality: Measuring factors like coherence, relevance, and factual accuracy
• Safety & Bias: Checking for harmful outputs, biases, and toxic content
• Domain Expertise: Testing specialized knowledge and capabilities in specific fields

Contents

Automatic Benchmarks

Learn how to evaluate your model using standardized benchmarks and metrics:

• Common benchmarks (MMLU, TruthfulQA, etc.)
• Evaluation metrics and settings
• Best practices for reproducible evaluation

Custom Domain Evaluation

Create custom evaluation pipelines for your specific use case:

• Designing evaluation tasks
• Implementing custom metrics
• Creating evaluation datasets

Domain Evaluation Project

A complete example of building a domain-specific evaluation pipeline:

• Generate evaluation datasets
• Annotate data with Argilla
• Create standardized datasets
• Evaluate models with LightEval

Resources

• Evaluation Guidebook
• LightEval Documentation
• Argilla Documentation