Add prefix to examples in model_doc rst (#11226)

* Add prefix to examples in model_doc rst

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/bertgeneration.rst

@@ -38,22 +38,22 @@ Usage:
 
 .. code-block::
 
-    # leverage checkpoints for Bert2Bert model...
-    # use BERT's cls token as BOS token and sep token as EOS token
-    encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
-    # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
-    decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
-    bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
+    >>> # leverage checkpoints for Bert2Bert model...
+    >>> # use BERT's cls token as BOS token and sep token as EOS token
+    >>> encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
+    >>> # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
+    >>> decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
+    >>> bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
 
-    # create tokenizer...
-    tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
+    >>> # create tokenizer...
+    >>> tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
 
-    input_ids = tokenizer('This is a long article to summarize', add_special_tokens=False, return_tensors="pt").input_ids
-    labels = tokenizer('This is a short summary', return_tensors="pt").input_ids
+    >>> input_ids = tokenizer('This is a long article to summarize', add_special_tokens=False, return_tensors="pt").input_ids
+    >>> labels = tokenizer('This is a short summary', return_tensors="pt").input_ids
 
-    # train...
-    loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
-    loss.backward()
+    >>> # train...
+    >>> loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
+    >>> loss.backward()
 
 
 - Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, e.g.,
@@ -61,15 +61,15 @@ Usage:
 
 .. code-block::
 
-    # instantiate sentence fusion model
-    sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
-    tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
+    >>> # instantiate sentence fusion model
+    >>> sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
+    >>> tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
 
-    input_ids = tokenizer('This is the first sentence. This is the second sentence.', add_special_tokens=False, return_tensors="pt").input_ids
+    >>> input_ids = tokenizer('This is the first sentence. This is the second sentence.', add_special_tokens=False, return_tensors="pt").input_ids
 
-    outputs = sentence_fuser.generate(input_ids)
+    >>> outputs = sentence_fuser.generate(input_ids)
 
-    print(tokenizer.decode(outputs[0]))
+    >>> print(tokenizer.decode(outputs[0]))
 
 
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