diff --git a/FlagEmbedding/baai_general_embedding/finetune/modeling.py b/FlagEmbedding/baai_general_embedding/finetune/modeling.py
index b25142e..7495553 100644
--- a/FlagEmbedding/baai_general_embedding/finetune/modeling.py
+++ b/FlagEmbedding/baai_general_embedding/finetune/modeling.py
@@ -86,6 +86,7 @@ class BiEncoderModel(nn.Module):
                 q_reps = self._dist_gather_tensor(q_reps)
                 p_reps = self._dist_gather_tensor(p_reps)
 
+
             scores = self.compute_similarity(q_reps, p_reps)
             scores = scores / self.temperature
             scores = scores.view(q_reps.size(0), -1)
@@ -94,6 +95,8 @@ class BiEncoderModel(nn.Module):
             target = target * (p_reps.size(0) // q_reps.size(0))
             loss = self.compute_loss(scores, target)
 
+
+
         else:
             scores = self.compute_similarity(q_reps, p_reps)
             loss = None
diff --git a/LM_Cocktail/LM_Cocktail/utils.py b/LM_Cocktail/LM_Cocktail/utils.py
index 47a5a49..c3422eb 100644
--- a/LM_Cocktail/LM_Cocktail/utils.py
+++ b/LM_Cocktail/LM_Cocktail/utils.py
@@ -156,6 +156,8 @@ def preprocess_data_for_embedder(example_data, tokenizer, device, batch_size:int
             p_tokens =  tokenizer(passages, padding=True, truncation=True, max_length=max_input_length, return_tensors="pt")
             q_tokens, p_tokens = q_tokens.to(device), p_tokens.to(device)
             input_data.append([q_tokens, p_tokens])
+            quries, passages = [], []
+
     if len(quries) > 0:
         q_tokens = tokenizer(quries, padding=True, truncation=True, max_length=max_input_length, return_tensors="pt")
         p_tokens =  tokenizer(passages, padding=True, truncation=True, max_length=max_input_length, return_tensors="pt")
diff --git a/LM_Cocktail/setup.py b/LM_Cocktail/setup.py
index 23baf0d..4793023 100644
--- a/LM_Cocktail/setup.py
+++ b/LM_Cocktail/setup.py
@@ -5,7 +5,7 @@ with open("README.md", mode="r", encoding="utf-8") as readme_file:
 
 setup(
     name='LM_Cocktail',
-    version='0.0.3',
+    version='0.0.4',
     description='LM_Cocktail',
     long_description=readme,
     long_description_content_type="text/markdown",