[Fix doc example] Wrong checkpoint name (#15079)

* fix doc example - MarianForCausalLM example

* try to keep copies

* fix copies

* fix more similar doc examples

* fix more

* fix style
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -1386,7 +1386,7 @@ class BlenderbotDecoderWrapper(BlenderbotPreTrainedModel):
         return self.decoder(*args, **kwargs)
 
 
-# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Blenderbot
+# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Blenderbot, facebook/bart-large->facebook/blenderbot-400M-distill
 class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
     def __init__(self, config):
         config = copy.deepcopy(config)
@@ -1508,8 +1508,10 @@ class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotTokenizer, BlenderbotForCausalLM
 
-        >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/bart-large")
-        >>> model = BlenderbotForCausalLM.from_pretrained("facebook/bart-large", add_cross_attention=False)
+        >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+        >>> model = BlenderbotForCausalLM.from_pretrained(
+        ...     "facebook/blenderbot-400M-distill", add_cross_attention=False
+        ... )
         >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -1357,7 +1357,7 @@ class BlenderbotSmallDecoderWrapper(BlenderbotSmallPreTrainedModel):
         return self.decoder(*args, **kwargs)
 
 
-# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->BlenderbotSmall
+# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->BlenderbotSmall, facebook/bart-large->facebook/blenderbot_small-90M
 class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
     def __init__(self, config):
         config = copy.deepcopy(config)
@@ -1479,8 +1479,10 @@ class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotSmallTokenizer, BlenderbotSmallForCausalLM
 
-        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained("facebook/bart-large")
-        >>> model = BlenderbotSmallForCausalLM.from_pretrained("facebook/bart-large", add_cross_attention=False)
+        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot_small-90M")
+        >>> model = BlenderbotSmallForCausalLM.from_pretrained(
+        ...     "facebook/blenderbot_small-90M", add_cross_attention=False
+        ... )
         >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/marian/modeling_marian.py

@@ -1397,7 +1397,7 @@ class MarianDecoderWrapper(MarianPreTrainedModel):
         return self.decoder(*args, **kwargs)
 
 
-# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Marian
+# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Marian, facebook/bart-large->Helsinki-NLP/opus-mt-fr-en
 class MarianForCausalLM(MarianPreTrainedModel):
     def __init__(self, config):
         config = copy.deepcopy(config)
@@ -1519,8 +1519,8 @@ class MarianForCausalLM(MarianPreTrainedModel):
         ```python
         >>> from transformers import MarianTokenizer, MarianForCausalLM
 
-        >>> tokenizer = MarianTokenizer.from_pretrained("facebook/bart-large")
-        >>> model = MarianForCausalLM.from_pretrained("facebook/bart-large", add_cross_attention=False)
+        >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
+        >>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en", add_cross_attention=False)
         >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/mbart/modeling_mbart.py

@@ -1662,7 +1662,7 @@ class MBartDecoderWrapper(MBartPreTrainedModel):
         return self.decoder(*args, **kwargs)
 
 
-# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->MBart
+# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->MBart, facebook/bart-large->facebook/mbart-large-cc25
 class MBartForCausalLM(MBartPreTrainedModel):
     def __init__(self, config):
         config = copy.deepcopy(config)
@@ -1784,8 +1784,8 @@ class MBartForCausalLM(MBartPreTrainedModel):
         ```python
         >>> from transformers import MBartTokenizer, MBartForCausalLM
 
-        >>> tokenizer = MBartTokenizer.from_pretrained("facebook/bart-large")
-        >>> model = MBartForCausalLM.from_pretrained("facebook/bart-large", add_cross_attention=False)
+        >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25")
+        >>> model = MBartForCausalLM.from_pretrained("facebook/mbart-large-cc25", add_cross_attention=False)
         >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/pegasus/modeling_pegasus.py

@@ -1537,7 +1537,7 @@ class PegasusForCausalLM(PegasusPreTrainedModel):
         self.model.decoder.resize_position_embeddings(new_num_position_embeddings)
 
     @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
-    # Copied from transformers.models.bart.modeling_bart.BartForCausalLM.forward with Bart->Pegasus
+    # Copied from transformers.models.bart.modeling_bart.BartForCausalLM.forward with Bart->Pegasus, facebook/bart-large->google/pegasus-large
     def forward(
         self,
         input_ids=None,
@@ -1627,8 +1627,8 @@ class PegasusForCausalLM(PegasusPreTrainedModel):
         ```python
         >>> from transformers import PegasusTokenizer, PegasusForCausalLM
 
-        >>> tokenizer = PegasusTokenizer.from_pretrained("facebook/bart-large")
-        >>> model = PegasusForCausalLM.from_pretrained("facebook/bart-large", add_cross_attention=False)
+        >>> tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-large")
+        >>> model = PegasusForCausalLM.from_pretrained("google/pegasus-large", add_cross_attention=False)
         >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)