update eval

evaluation/intro.txt

@@ -1,5 +1,5 @@
 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, and 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).
+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, PolyCoder, CodeGen and Codex (not open-source).
 
 
 | Model | pass@1 | pass@10 | pass@100|
@@ -9,12 +9,17 @@ In most papers, 200 candidate program completions are sampled, and pass@1, pass@
 |||||
 |InCoder (6.7B) | 15.2% | 27.8% | 47.00% |
 |||||
+|PolyCoder (160M)| 2.13% | 3.35% | 4.88% |
+|PolyCoder (400M)| 2.96% | 5.29% | 11.59% |
+|PolyCoder (2.7B)| 5.59% | 9.84% | 17.68% |
+|||||
+|CodeGen-Mono (350M)| 12.76% | 23.11% | 35.19% |
+|CodeGen-Mono (2.7B)| 23.70% | 36.64% | 57.01% |
+|CodeGen-Mono (16.1B)| **29.28%** | **49.86%** | **75.00%** |
+|||||
 |Codex (25M)| 3.21% | 7.1% |	12.89%|
 |Codex (300M)| 13.17%| 20.37% | 36.27% |
 |Codex (12B)| 28.81%| 46.81% | 72.31% |
-|||||
-|GPT-neo (1.5B)| 4.79% | 7.47% | 16.30% |
-|GPT-J (6B)| 11.62% | 15.74% | 27.74% |
 
 We can load HumanEval dataset and pass@k metric from 🤗 [`datasets`](https://huggingface.co/docs/datasets/index)