evaluating-code-models

To evaluate code generation models using pass@k metrics, use the BigCode Evaluation Harness to run standardized benchmarks across HumanEval, MBPP, and MultiPL-E datasets. The harness applies predefined YAML frontmatter configurations to generate reproducible quantitative performance scores.

Multi-language code model benchmarking supports HumanEval, MBPP, and MultiPL-E among 15+ open benchmarks. It evaluates code generation pipelines across 18 programming languages, delivering standardized pass@k results for broad comparative coverage.

Yes, you can use the BigCode Evaluation Harness to standardize model comparison results. It provides a unified evaluation pipeline with predefined task definitions and pass@k scoring, ensuring consistent and reproducible benchmarking across different code-generation models.

You configure custom evaluation tasks in the code generation harness using YAML frontmatter to declare task definitions. This frontmatter-driven configuration standardizes dataset loading and pass@k scoring within the unified evaluation pipeline.

The best way to benchmark an LLM across multiple programming languages is using the MultiPL-E benchmark within the BigCode Evaluation Harness. It evaluates code generation capabilities across 18 languages, producing quantitative pass@k metrics for broad coverage.