diff --git a/C_MTEB/eval_C-MTEB.py b/C_MTEB/eval_C-MTEB.py
index 0d176393..810d6abf 100644
--- a/C_MTEB/eval_C-MTEB.py
+++ b/C_MTEB/eval_C-MTEB.py
@@ -19,6 +19,8 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--model_name_or_path', default="BAAI/bge-large-zh", type=str)
     parser.add_argument('--task_type', default=None, type=str)
+    parser.add_argument('--add_instruction', action='store_true', help="whether to add instruction for query")
+    parser.add_argument('--pooling_method', default='cls', type=str)
     return parser.parse_args()
 
 
@@ -27,7 +29,8 @@ def get_args():
     args = get_args()
 
     model = FlagDRESModel(model_name_or_path=args.model_name_or_path,
-                          query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：")
+                          query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
+                          pooling_method=args.pooling_method)
 
     task_names = [t.description["name"] for t in MTEB(task_types=args.task_type,
                                                       task_langs=['zh', 'zh-CN']).tasks]
@@ -40,9 +43,11 @@ def get_args():
                     'EcomRetrieval', 'MedicalRetrieval', 'VideoRetrieval',
                     'T2Reranking', 'MMarcoReranking', 'CMedQAv1', 'CMedQAv2']:
             if args.model_name_or_path not in query_instruction_for_retrieval_dict:
-                instruction = "为这个句子生成表示以用于检索相关文章："
-                # instruction = None
-                print(f"{args.model_name_or_path} not in query_instruction_for_retrieval_dict, set instruction=为这个句子生成表示以用于检索相关文章：")
+                if args.add_instruction:
+                    instruction = "为这个句子生成表示以用于检索相关文章："
+                else:
+                    instruction = None
+                print(f"{args.model_name_or_path} not in query_instruction_for_retrieval_dict, set instruction={instruction}")
             else:
                 instruction = query_instruction_for_retrieval_dict[args.model_name_or_path]
         else:
diff --git a/C_MTEB/eval_MTEB.py b/C_MTEB/eval_MTEB.py
index d9159e52..f44cb9e9 100644
--- a/C_MTEB/eval_MTEB.py
+++ b/C_MTEB/eval_MTEB.py
@@ -18,6 +18,7 @@ def get_args():
     parser.add_argument('--model_name_or_path', default="BAAI/bge-large-en", type=str)
     parser.add_argument('--task_type', default=None, type=str, help="task type. Default is None, which means using all task types")
     parser.add_argument('--add_instruction', action='store_true', help="whether to add instruction for query")
+    parser.add_argument('--pooling_method', default='cls', type=str)
 
     return parser.parse_args()
 
@@ -27,7 +28,8 @@ def get_args():
 
     model = FlagDRESModel(model_name_or_path=args.model_name_or_path,
                           normalize_embeddings=False,  # normlize embedding will harm the performance of classification task
-                          query_instruction_for_retrieval="Represent this sentence for searching relevant passages: ")
+                          query_instruction_for_retrieval="Represent this sentence for searching relevant passages: ",
+                          pooling_method=args.pooling_method)
 
     task_names = [t.description["name"] for t in MTEB(task_types=args.task_type,
                                                       task_langs=['en']).tasks]
diff --git a/C_MTEB/flag_dres_model.py b/C_MTEB/flag_dres_model.py
index 67d95776..ce82cfa6 100644
--- a/C_MTEB/flag_dres_model.py
+++ b/C_MTEB/flag_dres_model.py
@@ -73,7 +73,7 @@ def encode(self, sentences: List[str], **kwargs) -> np.ndarray:
                 max_length=512,
             ).to(self.device)
             last_hidden_state = self.model(**inputs, return_dict=True).last_hidden_state
-            embeddings = last_hidden_state[:, 0]
+            embeddings = self.pooling(last_hidden_state, inputs['attention_mask'])
             if self.normalize_embeddings:
                 embeddings = torch.nn.functional.normalize(embeddings, dim=-1)
             embeddings = cast(torch.Tensor, embeddings)
@@ -81,9 +81,14 @@ def encode(self, sentences: List[str], **kwargs) -> np.ndarray:
 
         return np.concatenate(all_embeddings, axis=0)
 
-
-
-
-
+    def pooling(self,
+                last_hidden_state: torch.Tensor,
+                attention_mask: torch.Tensor=None):
+        if self.pooling_method == 'cls':
+            return last_hidden_state[:, 0]
+        elif self.pooling_method == 'mean':
+            s = torch.sum(last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1)
+            d = attention_mask.sum(dim=1, keepdim=True).float()
+            return s / d