Fix dimention misspellings.

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -155,8 +155,8 @@ def _get_block_length_and_num_blocks(seq_length, window_size):
     def _look_back(tensor, block_length, window_size, pad_value=0, is_key_value=True):
         """
         Used to implement attention between consecutive blocks. This method assumes that dim 1 of :obj:`tensor`
-        represents the :obj:`seq_length` dimention. It splits :obj:`seq_length` dimention into :obj:`num_blocks` and
-        :obj:`window_size` + :obj:`block_length`. It pads the :obj:`seq_length` dimention if necessary.
+        represents the :obj:`seq_length` dimension. It splits :obj:`seq_length` dimension into :obj:`num_blocks` and
+        :obj:`window_size` + :obj:`block_length`. It pads the :obj:`seq_length` dimension if necessary.
 
         Example::
 
@@ -373,7 +373,7 @@ def _create_attention_mask(self, batch_size, seq_length, num_blocks, block_lengt
         # look back into the attention_block such that it will also get padded the same way
         # and have 0s in the padded position
         attention_mask = self._look_back(attention_mask, block_length, self.window_size, is_key_value=False)
-        attention_mask = attention_mask.unsqueeze(-2)  # Add an extra dimention to account for hidden_dim
+        attention_mask = attention_mask.unsqueeze(-2)  # Add an extra dimension to account for hidden_dim
 
         # Multiply the causal_mask with attention_mask so the padded positions (by _look_back operation)
         # will contain 0s.
@@ -387,7 +387,7 @@ def _create_attention_mask(self, batch_size, seq_length, num_blocks, block_lengt
         visible = torch.gt(relative_position, -self.window_size)
 
         causal_mask = causal_mask * visible
-        causal_mask = causal_mask.unsqueeze(-3).bool()  # Add an extra dimention to account for num_heads
+        causal_mask = causal_mask.unsqueeze(-3).bool()  # Add an extra dimension to account for num_heads
 
         return causal_mask
 

src/transformers/models/speech_to_text/configuration_speech_to_text.py

@@ -90,7 +90,7 @@ class Speech2TextConfig(PretrainedConfig):
             An integer defining the number of output channels of each convolution layers except the final one in the
             conv module.
         input_feat_per_channel (:obj:`int`, `optional`, defaults to 80):
-            An integer specifying the size of feature vector. This is also the dimentions of log-mel filter-bank
+            An integer specifying the size of feature vector. This is also the dimensions of log-mel filter-bank
             features.
         input_channels (:obj:`int`, `optional`, defaults to 1):
             An integer specifying number of input channels of the input feature vector.