evaluating-code-models

Benchmark code generation models using a standardized harness across HumanEval, MBPP, and MultiPL-E datasets to measure pass@k metrics. It leverages HuggingFace datasets to deliver consistent and reproducible performance comparisons across languages.

The pass@k metric evaluates code generation quality by checking if at least one of k generated code samples passes unit tests. It is calculated using configurable settings like n_samples and temperature to measure model proficiency accurately.

Yes, you can evaluate HuggingFace transformers models across multiple programming languages using the MultiPL-E benchmark. This enables cross-language evaluation and consistent reporting for comparing model coding proficiency.

Yes, the BigCode evaluation harness provides the standardized framework needed to apply consistent evaluation modes and code-execution controls across 15+ benchmarks. It ensures reproducible metrics for comparing model capabilities over time.

You configure temperature and n_samples as parameters within the evaluation harness to control randomness and volume of generated code outputs. These settings directly impact the pass@k metric calculation and code-execution behavior.