fixed bert indenting (#875)

* fixed bert indenting

* fixed indentation

* fixed spacing

Author: bryce13950

transformer_lens/BertNextSentencePrediction.py

@@ -72,11 +72,10 @@ def to_tokens(
         sides or prepend_bos.
         Args:
             input: List[str]]: The input to tokenize.
-            move_to_device (bool): Whether to move the output tensor of tokens to the device the
-                model lives on. Defaults to True
+            move_to_device (bool): Whether to move the output tensor of tokens to the device the model lives on. Defaults to True
             truncate (bool): If the output tokens are too long, whether to truncate the output
-                tokens to the model's max context window. Does nothing for shorter inputs.
-                Defaults to True.
+            tokens to the model's max context window. Does nothing for shorter inputs. Defaults to
+            True.
         """
 
         if len(input) != 2:
@@ -143,8 +142,7 @@ def forward(
 
         Args:
             input: The input to process. Can be one of:
-                - List[str]: A list of two strings representing the two sentences NSP
-                                should be performed on
+                - List[str]: A list of two strings representing the two sentences NSP should be performed on
                 - torch.Tensor: Input tokens as integers with shape (batch, position)
             return_type: Optional[str]: The type of output to return. Can be one of:
                 - None: Return nothing, don't calculate logits

transformer_lens/HookedEncoder.py

@@ -102,11 +102,10 @@ def to_tokens(
         sides or prepend_bos.
         Args:
             input (Union[str, List[str]]): The input to tokenize.
-            move_to_device (bool): Whether to move the output tensor of tokens to the device the
-                model lives on. Defaults to True
+            move_to_device (bool): Whether to move the output tensor of tokens to the device the model lives on. Defaults to True
             truncate (bool): If the output tokens are too long, whether to truncate the output
-                tokens to the model's max context window. Does nothing for shorter inputs.
-                Defaults to True.
+            tokens to the model's max context window. Does nothing for shorter inputs. Defaults to
+            True.
         """
 
         assert self.tokenizer is not None, "Cannot use to_tokens without a tokenizer"