Fix examples: 'CausalLMOutputWithCrossAttentions' object has no attribute...

Fix examples: 'CausalLMOutputWithCrossAttentions' object has no attribute 'last_hidden_state' (#14678)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/bart/modeling_bart.py

@@ -1790,7 +1790,7 @@ class BartForCausalLM(BartPretrainedModel):
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -2993,7 +2993,7 @@ class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -1528,7 +1528,7 @@ class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -1502,7 +1502,7 @@ class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

src/transformers/models/marian/modeling_marian.py

@@ -1525,7 +1525,7 @@ class MarianForCausalLM(MarianPreTrainedModel):
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

src/transformers/models/mbart/modeling_mbart.py

@@ -1793,7 +1793,7 @@ class MBartForCausalLM(MBartPreTrainedModel):
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

src/transformers/models/pegasus/modeling_pegasus.py

@@ -1637,7 +1637,7 @@ class PegasusForCausalLM(PegasusPreTrainedModel):
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

src/transformers/models/tapas/modeling_tapas.py

@@ -1070,7 +1070,7 @@ class TapasForMaskedLM(TapasPreTrainedModel):
             >>> labels = tokenizer(table=table, queries="How many movies has George Clooney played in?", return_tensors="pt")["input_ids"]
 
             >>> outputs = model(**inputs, labels=labels)
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 

src/transformers/models/tapas/modeling_tf_tapas.py

@@ -1130,7 +1130,7 @@ class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss):
             >>> labels = tokenizer(table=table, queries="How many movies has George Clooney played in?", return_tensors="tf")["input_ids"]
 
             >>> outputs = model(**inputs, labels=labels)
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
         inputs = input_processing(
             func=self.call,

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -3294,7 +3294,7 @@ class {{cookiecutter.camelcase_modelname}}ForCausalLM({{cookiecutter.camelcase_m
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            >>> logits = outputs.logits
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions