evaluating-code-models

This Skill evaluates code generation models using the BigCode Evaluation Harness framework to report pass@k metrics on benchmarks like HumanEval. It provides a standardized framework to measure functional correctness across 18 different programming languages.

Supported benchmarks for comparing code generation models include HumanEval, MBPP, and MultiPL-E. The framework measures functional correctness using industry-standard pass@k metrics to facilitate comparative analysis of coding abilities across 18 languages.

Yes, the BigCode Evaluation Harness supports multi-language code benchmarking across 18 different programming languages using the MultiPL-E benchmark. This allows comprehensive evaluation of model coding abilities beyond standard Python-focused tests.

To calculate pass@k metrics for a custom model, run it through the BigCode Evaluation Harness framework within this Skill. It measures functional correctness against standard benchmarks like HumanEval and MBPP to generate the pass@k scores.

Required dependencies to run code generation benchmarking scripts include the BigCode Evaluation Harness, transformers (>=4.25.1), accelerate (>=0.13.2), and datasets (>=2.6.1). These libraries provide the core framework and model loading capabilities required for evaluation.