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evaluation/intro.txt

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-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).
+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.
+In most papers, 200 candidate program completions are sampled, and pass@1, pass@10, and pass@100 are computed using an unbiased sampling estimator. The table below shows the HumanEval scores of CodeParrot, InCoder, GPT-neo, GPT-J and Codex (not open-source).
 
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