recover pre-commit and edit math expr in doc

.pre-commit-config-zh-cn.yaml

@@ -88,12 +88,7 @@ repos:
           - mdformat-openmmlab
           - mdformat_frontmatter
           - linkify-it-py
-        exclude: |
-          (?x)^(
-            configs/ |
-            docs/zh_cn/user_guides/datasets.md |
-            docs/en/user_guides/datasets.md
-          )
+        exclude: configs/
   - repo: https://gitee.com/openmmlab/mirrors-docformatter
     rev: v1.3.1
     hooks:

.pre-commit-config.yaml

@@ -88,12 +88,7 @@ repos:
           - mdformat-openmmlab
           - mdformat_frontmatter
           - linkify-it-py
-        exclude: |
-          (?x)^(
-            configs/ |
-            docs/zh_cn/user_guides/datasets.md |
-            docs/en/user_guides/datasets.md
-          )
+        exclude: configs/
   - repo: https://github.com/myint/docformatter
     rev: v1.3.1
     hooks:

docs/en/user_guides/datasets.md

@@ -82,7 +82,6 @@ Users can choose different abilities, different datasets and different evaluatio
 
 For information on how to start an evaluation task and how to evaluate self-built datasets, please refer to the relevant documents.
 
-
 ### Multiple Evaluations on the Dataset
 
 In the dataset configuration, you can set the parameter `n` to perform multiple evaluations on the same dataset and return the average metrics, for example:
@@ -99,11 +98,15 @@ afqmc_datasets = [
         eval_cfg=afqmc_eval_cfg,
     ),
 ]
+
 ```
-> [!TIP]
+
+> \[!TIP\]
 > Additionally, for binary evaluation metrics (such as accuracy, pass-rate, etc.), you can also set the parameter `k` in conjunction with `n` for [G-Pass@k](http://arxiv.org/abs/2412.13147) evaluation. The formula for G-Pass@k is:
 >
-> $$ \text{G-Pass@}k_\tau=E_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right], $$ 
+> ```{math}
+> \text{G-Pass@}k_\tau=E_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right], 
+> ```
 >
 > where $n$ is the number of evaluations, and $c$ is the number of times that passed or were correct out of $n$ runs. An example configuration is as follows:
 
@@ -118,4 +121,4 @@ aime2024_datasets = [
         ...
     )
 ]
-```
+```

docs/zh_cn/user_guides/datasets.md

@@ -100,10 +100,12 @@ afqmc_datasets = [
 ]
 ```
 
-> [!TIP]
-> 另外，对于二值评测指标（例如accuracy，pass-rate等），还可以通过设置参数`k`配合`n`进行[G-Pass@k](http://arxiv.org/abs/2412.13147)评测。G-Pass@k计算公式为： 
-> 
-> $$ \text{G-Pass@}k_\tau=E_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right], $$ 
+> \[!TIP\]
+> 另外，对于二值评测指标（例如accuracy，pass-rate等），还可以通过设置参数`k`配合`n`进行[G-Pass@k](http://arxiv.org/abs/2412.13147)评测。G-Pass@k计算公式为：
+>
+> ```{math}
+> \text{G-Pass@}k_\tau=E_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right], 
+> ```
 >
 > 其中 $n$ 为评测次数, $c$ 为 $n$ 次运行中通过或正确的次数。配置例子如下：