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@@ -174,6 +174,12 @@ Below are zero-shot and five-shot accuracies from the models that we evaluate in
   print(dataset['val'][0])
   # {"id": 1, "question": "单元测试应该覆盖以下哪些方面？", "A": "正常路径", "B": "异常路径", "C": "边界值条件"，"D": 所有以上，"answer": "D", "explanation": ""}  ```
 
+* Method 3: Directly load the datase t using [ModelScope datasets](https://modelscope.cn/datasets/codefuse-ai/devopseval-exam/files):
+  ```python
+  from modelscope.msdatasets import MsDataset
+  MsDataset.clone_meta(dataset_work_dir='./xxx', dataset_id='codefuse-ai/devopseval-exam')
+  ```
+
 #### 👀 Notes
 To facilitate usage, we have organized the category name handlers and English/Chinese names corresponding to 55 subcategories. Please refer to [category_mapping.json](resources/categroy_mapping.json) for details. The format is:
 
 
@@ -177,7 +177,8 @@ DevOps-Eval是一个专门为DevOps领域大模型设计的综合评估数据集
 * 方法三：使用modelscope下载相关所有数据。示例如下：
   ```python
   from modelscope.msdatasets import MsDataset
-  MsDataset.clone_meta(dataset_work_dir='./xxx', dataset_id='codefuse-ai/devopseval-exam')```
+  MsDataset.clone_meta(dataset_work_dir='./xxx', dataset_id='codefuse-ai/devopseval-exam')
+  ```
 
 #### 👀 说明
 为了方便使用，我们已经整理出了 55 个细分类别以及它们的中英文名称。具体细节请查看 [category_mapping.json](resources/categroy_mapping.json) 。格式如下：