Correcting examples: Replacing output_attention with its plural form

bertviz/transformers_neuron_view/modeling_utils.py

@@ -137,11 +137,11 @@ def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
             config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
             config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
             config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
-            config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
-            assert config.output_attention == True
-            config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
+            config = BertConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)
+            assert config.output_attentions == True
+            config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attentions=True,
                                                                foo=False, return_unused_kwargs=True)
-            assert config.output_attention == True
+            assert config.output_attentions == True
             assert unused_kwargs == {'foo': False}
 
         """
@@ -405,7 +405,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                 Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
 
             kwargs: (`optional`) Remaining dictionary of keyword arguments:
-                Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
+                Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attentions=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                 - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
@@ -414,8 +414,8 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
 
             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/')`
-            model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
-            assert model.config.output_attention == True
+            model = BertModel.from_pretrained('bert-base-uncased', output_attentions=True)  # Update configuration during loading
+            assert model.config.output_attentions == True
             # Loading from a TF checkpoint file instead of a PyTorch model (slower)
             config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
             model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)