diff --git a/libs/community/langchain_community/cross_encoders/huggingface.py b/libs/community/langchain_community/cross_encoders/huggingface.py
index a0d1f68a1..b6b0498e3 100644
--- a/libs/community/langchain_community/cross_encoders/huggingface.py
+++ b/libs/community/langchain_community/cross_encoders/huggingface.py
@@ -1,20 +1,15 @@
 from typing import Any, Dict, List, Tuple
-
 from pydantic import BaseModel, ConfigDict, Field
-
 from langchain_community.cross_encoders.base import BaseCrossEncoder
 
 DEFAULT_MODEL_NAME = "BAAI/bge-reranker-base"
 
-
 class HuggingFaceCrossEncoder(BaseModel, BaseCrossEncoder):
     """HuggingFace cross encoder models.
-
+    
     Example:
         .. code-block:: python
-
             from langchain_community.cross_encoders import HuggingFaceCrossEncoder
-
             model_name = "BAAI/bge-reranker-base"
             model_kwargs = {'device': 'cpu'}
             hf = HuggingFaceCrossEncoder(
@@ -22,43 +17,75 @@ class HuggingFaceCrossEncoder(BaseModel, BaseCrossEncoder):
                 model_kwargs=model_kwargs
             )
     """
-
+    
     client: Any = None  #: :meta private:
+    tokenizer: Any = None  #: :meta private:
     model_name: str = DEFAULT_MODEL_NAME
     """Model name to use."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Keyword arguments to pass to the model."""
-
+    
     def __init__(self, **kwargs: Any):
         """Initialize the sentence_transformer."""
         super().__init__(**kwargs)
         try:
             import sentence_transformers
-
         except ImportError as exc:
             raise ImportError(
                 "Could not import sentence_transformers python package. "
                 "Please install it with `pip install sentence-transformers`."
             ) from exc
-
+        
         self.client = sentence_transformers.CrossEncoder(
             self.model_name, **self.model_kwargs
         )
-
+        
+        # Store tokenizer reference for pad token management
+        self.tokenizer = self.client.tokenizer
+        
+        # Ensure padding token is available
+        self._ensure_padding_token()
+    
+    def _ensure_padding_token(self):
+        """Ensure that a padding token is available for the tokenizer."""
+        if self.tokenizer.pad_token is None:
+            if self.tokenizer.eos_token:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+                self.client.config.pad_token_id = self.tokenizer.eos_token_id
+            elif hasattr(self.tokenizer, 'unk_token') and self.tokenizer.unk_token:
+                self.tokenizer.pad_token = self.tokenizer.unk_token
+                self.client.config.pad_token_id = self.tokenizer.unk_token_id
+            else:
+                self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+                self.client.resize_token_embeddings(len(self.tokenizer))
+                self.client.config.pad_token_id = self.tokenizer.pad_token_id
+    
     model_config = ConfigDict(extra="forbid", protected_namespaces=())
-
+    
     def score(self, text_pairs: List[Tuple[str, str]]) -> List[float]:
         """Compute similarity scores using a HuggingFace transformer model.
-
+        
         Args:
             text_pairs: The list of text text_pairs to score the similarity.
-
+        
         Returns:
             List of scores, one for each pair.
         """
-        scores = self.client.predict(text_pairs)
+        try:
+            scores = self.client.predict(text_pairs)
+        except ValueError as e:
+            if "Cannot handle batch sizes > 1" in str(e) or "pad_token" in str(e).lower():
+                # Fallback to processing pairs individually for models without pad_token
+                scores = []
+                for pair in text_pairs:
+                    score = self.client.predict([pair])
+                    scores.extend(score if isinstance(score, list) else [score])
+            else:
+                raise e
+        
         # Some models e.g bert-multilingual-passage-reranking-msmarco
         # gives two score not_relevant and relevant as compare with the query.
-        if len(scores.shape) > 1:  # we are going to get the relevant scores
-            scores = map(lambda x: x[1], scores)
+        if hasattr(scores, 'shape') and len(scores.shape) > 1:  # we are going to get the relevant scores
+            scores = [x[1] for x in scores]
+        
         return scores