| A popular evaluation framework for code generation models is the [pass@k](https://huggingface.co/metrics/code_eval) metric on [HumanEval](https://huggingface.co/datasets/openai_humaneval) dataset, which was introduced in [Codex paper](https://arxiv.org/pdf/2107.03374v2.pdf). The dataset includes 164 handwritten programming problems. In the pass@k metric, k code samples are generated per problem, a problem is considered solved if any sample passes the unit tests and the total fraction of problems solved is reported. Below are some examples for the selcted models. | |
| For most models, we sample 200 candidate program completions, and compute pass@1, pass@10, and pass@100 using an unbiased sampling estimator. The table below shows the humanEval scores of CodeParrot, InCoder, GPT-neo models, GPT-J and Codex (not open-source). | |
| | Model | pass@1 | pass@10 | pass@100| | |
| |-------|--------|---------|---------| | |
| |CodeParrot 🦜 (110M) | 3.80% | 6.57% | 12.78% | | |
| |CodeParrot 🦜 (1.5B) | 3.58% | 8.03% | 14.96% | | |
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| |InCoder 🦜 (6.7B) | 15.2% | 27.8% | 47.00% | | |
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| |Codex (25M)| 3.21% | 7.1% | 12.89%| | |
| |Codex (85M)| 8.22% | 12.81% | 22.40% | | |
| |Codex (300M)| 13.17%| 20.37% | 36.27% | | |
| |Codex (12B)| 28.81%| 46.81% | 72.31% | | |
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| |GPT-neo (125M)| 0.75% | 1.88% | 2.97% | | |
| |GPT-neo (1.5B)| 4.79% | 7.47% | 16.30% | | |
| |GPT-neo (2.7B)| 6.41% | 11.27% | 21.37% | | |
| |GPT-J (6B)| 11.62% | 15.74% | 27.74% | |