evaluating-code-models

This Skill evaluates code generation models using pass@k metrics across multiple benchmarks. It measures functional correctness and code generation quality by testing models against standardized datasets like HumanEval and MBPP.

Yes, you can benchmark code generation models across 18 programming languages using the MultiPL-E benchmark. This Skill supports multi-language evaluation to comprehensively assess model performance in different language environments.

Supported benchmarks for testing code generation models include HumanEval, MBPP, MultiPL-E, and over 15 other industry-standard frameworks. These provide a standardized evaluation framework for objective comparison and quality assessment.

Yes, installing the bigcode-evaluation-harness library and associated Python dependencies like transformers and accelerate is required. This setup provides the necessary environment to execute code model evaluation tasks and generate pass@k scores.

The best way to compare two code generation models objectively is to run both against the same benchmarks, such as HumanEval and MBPP. This Skill generates clear pass@k scores for each model to determine which performs better.