update eng doc for multi-run and g-pass

docs/en/user_guides/datasets.md

@@ -81,3 +81,38 @@ datasets += cmnli_datasets
 Users can choose different abilities, different datasets and different evaluation methods configuration files to build the part of the dataset in the evaluation script according to their needs.
 
 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:
+
+```python
+afqmc_datasets = [
+    dict(
+        abbr="afqmc-dev",
+        type=AFQMCDatasetV2,
+        path="./data/CLUE/AFQMC/dev.json",
+        n=10, # Perform 10 evaluations
+        reader_cfg=afqmc_reader_cfg,
+        infer_cfg=afqmc_infer_cfg,
+        eval_cfg=afqmc_eval_cfg,
+    ),
+]
+```
+
+> [!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: $$\text{G-Pass@}k_\tau=\mathbb{E}_{\text{Data}}\left[ \sum_{j=\lceil \tau \cdot k \rceil}^c \frac{{c \choose j} \cdot {n - c \choose k - j}}{{n \choose k}} \right],$$ 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:
+
+```python
+aime2024_datasets = [
+    dict(
+        abbr='aime2024',
+        type=Aime2024Dataset,
+        path='opencompass/aime2024',
+        k=[2, 4], # Return results for G-Pass@2 and G-Pass@4
+        n=12, # 12 evaluations
+        ...
+    )
+]
+```

docs/zh_cn/user_guides/datasets.md

@@ -101,7 +101,7 @@ 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=\mathbb{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],$$ 其中 $n$ 为评测次数, $c$ 为 $n$ 次运行中通过或正确的次数。配置例子如下：
+> 另外，对于二值评测指标（例如accuracy，pass-rate等），还可以通过设置参数`k`配合`n`进行[G-Pass@$k$](http://arxiv.org/abs/2412.13147)评测。G-Pass@$k$计算公式为：$$\text{G-Pass@}k_\tau=\mathbb{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：
 
 ```python
 aime2024_datasets = [