evaluating-code-models

pass@k metrics measure the probability that at least one of the top k generated code samples passes the unit tests for a given benchmark task. They are the industry standard for evaluating code generation models on benchmarks like HumanEval and MBPP.

To evaluate a code generation model, launch the evaluation harness using accelerate with your chosen transformers model, specify tasks like humaneval, set generation parameters, and allow code execution. This runs the model against the benchmarks and saves the generated outputs.

Yes, the BigCode evaluation harness supports MultiPL-E, allowing you to evaluate code generation models across multiple programming languages. It includes 15+ benchmarks to comprehensively test multi-language coding capabilities using pass@k metrics.

To run pass@k evaluations for code generation, you need the bigcode-evaluation-harness along with transformers, accelerate, and datasets libraries. These dependencies facilitate model loading, distributed execution, and benchmark dataset processing.

Allowing code execution is required to verify the functional correctness of the generated code samples against the benchmark unit tests. The evaluation harness executes the generated code to calculate accurate pass@k metrics for models on benchmarks like HumanEval and MBPP.