diff --git a/clip/clip.py b/clip/clip.py
index 55e1433..974ef06 100644
--- a/clip/clip.py
+++ b/clip/clip.py
@@ -180,7 +180,7 @@ def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_a
     return model, _transform(model.input_resolution.item())
 
 
-def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor:
+def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> torch.LongTensor:
     """
     Returns the tokenized representation of given input string(s)
 
@@ -192,6 +192,9 @@ def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.Lo
     context_length : int
         The context length to use; all CLIP models use 77 as the context length
 
+    truncate: bool
+        Whether to truncate the text in case its encoding is longer than the context length
+
     Returns
     -------
     A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
@@ -206,7 +209,11 @@ def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.Lo
 
     for i, tokens in enumerate(all_tokens):
         if len(tokens) > context_length:
-            raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
+            if truncate:
+                tokens = tokens[:context_length]
+                tokens[-1] = eot_token
+            else:
+                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
         result[i, :len(tokens)] = torch.tensor(tokens)
 
     return result