evaluating-code-models

To evaluate code generation models, you can benchmark them against HumanEval and MBPP datasets to measure functional correctness using pass@k metrics. This requires installing the bigcode-evaluation-harness library to automate testing and report performance scores.

Yes, you can benchmark LLMs across 18 programming languages using the MultiPL-E dataset. This multi-language evaluation extends standard code generation benchmarks to provide a comprehensive performance analysis for your models.

The best way to compare code generation models is running them through standardized benchmarks like HumanEval and analyzing their respective pass@1 scores. This provides quantitative pass@k metrics for functional correctness and objective comparison.

Yes, installing the bigcode-evaluation-harness library and its dependencies is required for pass@k benchmarking. It acts as the core framework for evaluating code generation models across supported datasets like HumanEval and MBPP.

Pass@k metrics work by measuring the functional correctness of generated code samples against test cases in benchmarks like HumanEval. It calculates the probability that at least one of k generated code samples passes the execution tests.

Yes, the evaluation supports models loaded with HuggingFace transformers and accelerate. These dependencies integrate with the bigcode-evaluation-harness to execute and benchmark large language models across various datasets.