dictionnary => dictionary

pytorch_transformers/modeling_utils.py

@@ -358,7 +358,7 @@ class PreTrainedModel(nn.Module):
                 Dictionary of key, values to update the configuration object after loading.
                 Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
 
-               - If a configuration is provided with `config`, **kwargs will be directly passed
+               - If a configuration is providedictionaryfig`, **kwargs will be directly passed
                  to the underlying model's __init__ method.
                - If a configuration is not provided, **kwargs will be first passed to the pretrained
                  model configuration class loading function (`PretrainedConfig.from_pretrained`).
@@ -367,7 +367,7 @@ class PreTrainedModel(nn.Module):
                  Remaining keys that do not correspond to any configuration attribute will
                  be passed to the underlying model's __init__ function.
 
-        Examples::
+        Examples::dictionary
 
             >>> model = BertModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
             >>> model = BertModel.from_pretrained('./test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`

pytorch_transformers/tokenization_utils.py

@@ -37,7 +37,7 @@ class PreTrainedTokenizer(object):
             additional_special_tokens = []
 
         We defined an added_tokens_encoder to add new tokens to the vocabulary without having to handle the
-            specific vocabulary augmentation methods of the various underlying dictionnary structures (BPE, sentencepiece...).
+            specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
     """
     vocab_files_names = {}
     pretrained_vocab_files_map = {}
@@ -324,7 +324,7 @@ class PreTrainedTokenizer(object):
 
 
     def add_special_tokens(self, special_tokens_dict):
-        """ Add a dictionnary of special tokens (eos, pad, cls...) to the encoder and link them
+        """ Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
             to class attributes. If the special tokens are not in the vocabulary, they are added
             to it and indexed starting from the last index of the current vocabulary.