Comments (4)
rongaoli
commented on September 28, 2024
Results reported in paper was only on the TACO-test split.
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ccccj
commented on September 28, 2024
不好意思，不知道是否是我的理解有误，论文中使用的是pass@k，我理解的是，pass@1就是生成1次代码，通过的个数再除以总的问题个数，如果是只在test上测的，test split一共1000个问题，以 codellama-7b-python 的 pass@1 为例，是9.32%，通过的总个数是93.2个？为什么会有小数呢？不知道是我哪里理解错了
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rongaoli
commented on September 28, 2024
We compute Pass@k according to formula mentioned in Codex Paper.
I think it will help you :)
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ccccj
commented on September 28, 2024
原来如此！我对这个式子的理解有些问题，感谢您~
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Related Issues (11)
- compute_metric.py 好像有问题 HOT 3
- 更新后的评测框架似乎存在重大bug? HOT 2
- How to construct the appropriate output? HOT 1
- AttributeError: module 'inspect' has no attribute 'getargspec'. Did you mean: 'getargs'? HOT 1
- specific performance of gpt-4 HOT 1
- code-llama-7b-python精度对不上 HOT 3
- 数据污染问题 HOT 1
- 题目的难度是如何确定的？/ How is the difficulty level obtained? HOT 2
- 请问TACO数据集中是否包含了APPS和code_contest中的所有题目？ HOT 1
- Finetuned Models HOT 3
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