evaluating-code-models

To evaluate code generation models, this Skill uses the BigCode Evaluation Harness to test models against benchmarks like HumanEval and MBPP, measuring functional correctness with pass@k metrics to provide detailed performance analysis.

Benchmarking code generation models supports over 15 benchmarks including HumanEval, MBPP, and MultiPL-E, enabling multi-language evaluation across 18 different programming languages using the BigCode Evaluation Harness.

Yes, measuring pass@k metrics requires installing dependencies like transformers, accelerate, and datasets, which provide the necessary environment for running the BigCode Evaluation Harness and loading models for evaluation.

Yes, you can run multi-language evaluation for code models across 18 different programming languages using the MultiPL-E benchmark within the BigCode Evaluation Harness to measure functional correctness.

The best way to compare code generation models is using a standardized evaluation framework that calculates pass@k metrics on benchmarks like HumanEval and MBPP, ensuring consistent performance analysis against state-of-the-art models.

The pass@k metric measures functional correctness by calculating the probability that at least one of k generated code samples passes the test cases for a given problem, providing an industry-standard evaluation of code generation models.