evaluating-code-models

To benchmark code generation models using HumanEval and MBPP, this Skill automates the evaluation process and measures functional correctness with pass@k metrics. It provides objective performance comparisons across these standardized benchmarks.

The pass@k metric in code generation evaluation measures functional correctness by calculating the probability that at least one of k generated samples passes the tests. This Skill utilizes it as an industry standard for benchmarking.

Yes, you can evaluate multi-language code generation across 18 different programming languages using the MultiPL-E benchmark. This Skill facilitates testing multi-language support to measure code generation quality comprehensively.

The BigCode Evaluation Harness supports over 15 standardized benchmarks including HumanEval, MBPP, and MultiPL-E. While it provides extensive multi-benchmark evaluation, custom benchmark integration depends on the harness's configuration capabilities.

To run code generation benchmarks, you need the bigcode-evaluation-harness along with transformers, accelerate, and datasets libraries. These dependencies provide the environment for loading models and executing standardized benchmark evaluations.