Refactor Code samples; Test code samples (#5036)

* Refactor code samples

* Test docstrings

* Style

* Tokenization examples

* Run rust of tests

* First step to testing source docs

* Style and BART comment

* Test the remainder of the code samples

* Style

* let to const

* Formatting fixes

* Ready for merge

* Fix fixture + Style

* Fix last tests

* Update docs/source/quicktour.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Addressing @sgugger's comments + Fix MobileBERT in TF
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/_static/css/code-snippets.css

@@ -9,4 +9,8 @@
 
 .highlight .kn, .highlight .nv, .highlight .s2, .highlight .ow {
     color: #6670FF;
+}
+
+.highlight .gp {
+    color: #FB8D68;
 }
\ No newline at end of file

docs/source/_static/css/huggingface.css

@@ -44,6 +44,7 @@
     display: flex;
     flex-direction: row;
     justify-content: flex-end;
+    margin-right: 30px;
 }
 
 .framework-selector > button {
@@ -60,6 +61,12 @@
     padding: 5px;
 }
 
+/* Copy button */
+
+a.copybtn {
+    margin: 3px;
+}
+
 /* The literal code blocks */
 .rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
     color: #6670FF;

docs/source/_static/js/custom.js

@@ -157,6 +157,8 @@ function platformToggle() {
     const codeBlocks = Array.from(document.getElementsByClassName("highlight"));
     const pytorchIdentifier = "## PYTORCH CODE";
     const tensorflowIdentifier = "## TENSORFLOW CODE";
+
+    const promptSpanIdentifier = `<span class="gp">&gt;&gt;&gt; </span>`
     const pytorchSpanIdentifier = `<span class="c1">${pytorchIdentifier}</span>`;
     const tensorflowSpanIdentifier = `<span class="c1">${tensorflowIdentifier}</span>`;
 
@@ -169,10 +171,22 @@ function platformToggle() {
         let tensorflowSpans;
 
         if(pytorchSpanPosition < tensorflowSpanPosition){
-            pytorchSpans = spans.slice(pytorchSpanPosition + pytorchSpanIdentifier.length + 1, tensorflowSpanPosition);
+            const isPrompt = spans.slice(
+                spans.indexOf(tensorflowSpanIdentifier) - promptSpanIdentifier.length,
+                spans.indexOf(tensorflowSpanIdentifier)
+            ) == promptSpanIdentifier;
+            const finalTensorflowSpanPosition = isPrompt ? tensorflowSpanPosition - promptSpanIdentifier.length : tensorflowSpanPosition;
+
+            pytorchSpans = spans.slice(pytorchSpanPosition + pytorchSpanIdentifier.length + 1, finalTensorflowSpanPosition);
             tensorflowSpans = spans.slice(tensorflowSpanPosition + tensorflowSpanIdentifier.length + 1, spans.length);
         }else{
-            tensorflowSpans = spans.slice(tensorflowSpanPosition + tensorflowSpanIdentifier.length + 1, pytorchSpanPosition);
+            const isPrompt = spans.slice(
+                spans.indexOf(pytorchSpanIdentifier) - promptSpanIdentifier.length,
+                spans.indexOf(pytorchSpanIdentifier)
+            ) == promptSpanIdentifier;
+            const finalPytorchSpanPosition = isPrompt ? pytorchSpanPosition - promptSpanIdentifier.length : pytorchSpanPosition;
+
+            tensorflowSpans = spans.slice(tensorflowSpanPosition + tensorflowSpanIdentifier.length + 1, finalPytorchSpanPosition);
             pytorchSpans = spans.slice(pytorchSpanPosition + pytorchSpanIdentifier.length + 1, spans.length);
         }
 

docs/source/conf.py

@@ -44,7 +44,8 @@ extensions = [
     'sphinx.ext.napoleon',
     'recommonmark',
     'sphinx.ext.viewcode',
-    'sphinx_markdown_tables'
+    'sphinx_markdown_tables',
+    'sphinx_copybutton'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -74,6 +75,8 @@ exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = None
 
+# Remove the prompt when copying examples
+copybutton_prompt_text = ">>> "
 
 # -- Options for HTML output -------------------------------------------------
 

docs/source/glossary.rst

@@ -45,17 +45,16 @@ tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ token
 
 ::
 
-    from transformers import BertTokenizer
-    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+    >>> from transformers import BertTokenizer
+    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
 
-    sequence = "A Titan RTX has 24GB of VRAM"
+    >>> sequence = "A Titan RTX has 24GB of VRAM"
 
 The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
 
 ::
 
-    tokenized_sequence = tokenizer.tokenize(sequence)
-    print(tokenized_sequence)
+    >>> tokenized_sequence = tokenizer.tokenize(sequence)
 
 The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
 in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-dash is
@@ -63,6 +62,7 @@ added for "RA" and "M":
 
 ::
 
+    >>> print(tokenized_sequence)
     ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
 
 These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding
@@ -71,14 +71,14 @@ the sentence to the tokenizer, which leverages the Rust implementation of
 
 ::
 
-    encoded_sequence = tokenizer(sequence)["input_ids"]
-    print(encoded_sequence)
+    >>> encoded_sequence = tokenizer(sequence)["input_ids"]
 
 The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
 token indices are under the key "input_ids":
 
 ::
 
+    >>> print(encoded_sequence)
     [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
 
 Note that the tokenizer automatically adds "special tokens" (if the associated model rely on them) which are special
@@ -86,13 +86,14 @@ IDs the model sometimes uses. If we decode the previous sequence of ids,
 
 ::
 
-    tokenizer.decode(encoded_sequence)
+    >>> decoded_sequence = tokenizer.decode(encoded_sequence)
 
 we will see 
 
 ::
 
-    '[CLS] A Titan RTX has 24GB of VRAM [SEP]'
+    >>> print(decoded_sequence)
+    [CLS] A Titan RTX has 24GB of VRAM [SEP]
 
 because this is the way a :class:`~transformers.BertModel` is going to expect its inputs.
 
@@ -108,21 +109,20 @@ For example, consider these two sequences:
 
 ::
 
-    from transformers import BertTokenizer
-    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+    >>> from transformers import BertTokenizer
+    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
 
-    sequence_a = "This is a short sequence."
-    sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
+    >>> sequence_a = "This is a short sequence."
+    >>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
 
-    encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
-    encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
-    
-    len(encoded_sequence_a), len(encoded_sequence_b)
+    >>> encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
+    >>> encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
 
 The encoded versions have different lengths:
 
 ::
 
+    >>> len(encoded_sequence_a), len(encoded_sequence_b)
     (8, 19)
 
 Therefore, we can't be put then together in a same tensor as-is. The first sequence needs to be padded up to the length
@@ -133,15 +133,14 @@ it to pad like this:
 
 ::
 
-    padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
-    padded_sequences["input_ids"]
+    >>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
 
 We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
 
 ::
 
-    [[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-     [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
+    >>> padded_sequences["input_ids"]
+    [[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
 
 This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating
 the position of the padded indices so that the model does not attend to them. For the
@@ -150,14 +149,8 @@ a padded value. This attention mask is in the dictionary returned by the tokeniz
 
 ::
 
-    padded_sequences["attention_mask"]
-
-will give back
-
-::
-
-    [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
+    >>> padded_sequences["attention_mask"]
+    [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
 
 .. _token-type-ids:
 
@@ -170,26 +163,27 @@ tokens. For example, the BERT model builds its two sequence input as such:
 
 ::
 
-   # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
+   >>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
 
 We can use our tokenizer to automatically generate such a sentence by passing the two sequences as two arguments (and
 not a list like before) like this:
 
 ::
 
-    from transformers import BertTokenizer
-    tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
-    sequence_a = "HuggingFace is based in NYC"
-    sequence_b = "Where is HuggingFace based?"
+    >>> from transformers import BertTokenizer
+    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+    >>> sequence_a = "HuggingFace is based in NYC"
+    >>> sequence_b = "Where is HuggingFace based?"
 
-    encoded_dict = tokenizer(sequence_a, sequence_b)
-    tokenizer.decode(encoded_dict["input_ids"])
+    >>> encoded_dict = tokenizer(sequence_a, sequence_b)
+    >>> decoded = tokenizer.decode(encoded_dict["input_ids"])
 
 which will return:
 
 ::
 
-    "[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]"
+    >>> print(decoded)
+    [CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]
 
 This is enough for some models to understand where one sequence ends and where another begins. However, other models
 such as BERT have an additional mechanism, which are the token type IDs (also called segment IDs). They are a binary
@@ -199,12 +193,7 @@ The tokenizer returns in the dictionary under the key "token_type_ids":
 
 ::
 
-    encoded_dict['token_type_ids']
-
-will return
-
-::
-
+    >>> encoded_dict['token_type_ids']
     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
 
 The first sequence, the "context" used for the question, has all its tokens represented by :obj:`0`, whereas the

docs/source/multilingual.rst

@@ -36,10 +36,11 @@ Here is an example using the ``xlm-clm-enfr-1024`` checkpoint (Causal language m
 
 .. code-block::
 
-    import torch
-    from transformers import XLMTokenizer, XLMWithLMHeadModel
+    >>> import torch
+    >>> from transformers import XLMTokenizer, XLMWithLMHeadModel
 
-    tokenizer = XLMTokenizer.from_pretrained("xlm-clm-1024-enfr")
+    >>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
+    >>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
 
 
 The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
@@ -47,16 +48,15 @@ The different languages this model/tokenizer handles, as well as the ids of thes
 
 .. code-block::
 
-    # Continuation of the previous script
-    print(tokenizer.lang2id)  # {'en': 0, 'fr': 1}
+    >>> print(tokenizer.lang2id)
+    {'en': 0, 'fr': 1}
 
 
 These ids should be used when passing a language parameter during a model pass. Let's define our inputs:
 
 .. code-block::
 
-    # Continuation of the previous script
-    input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
+    >>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
 
 
 We should now define the language embedding by using the previously defined language id. We want to create a tensor
@@ -64,20 +64,18 @@ filled with the appropriate language ids, of the same size as input_ids. For eng
 
 .. code-block::
 
-    # Continuation of the previous script
-    language_id = tokenizer.lang2id['en']  # 0
-    langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
+    >>> language_id = tokenizer.lang2id['en']  # 0
+    >>> langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
 
-    # We reshape it to be of size (batch_size, sequence_length)
-    langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
+    >>> # We reshape it to be of size (batch_size, sequence_length)
+    >>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
 
 
 You can then feed it all as input to your model:
 
 .. code-block::
 
-    # Continuation of the previous script
-    outputs = model(input_ids, langs=langs)
+    >>> outputs = model(input_ids, langs=langs)
 
 
 The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py>`__

setup.py

@@ -86,7 +86,7 @@ extras["all"] = extras["serving"] + ["tensorflow", "torch"]
 
 extras["testing"] = ["pytest", "pytest-xdist", "timeout-decorator", "psutil"]
 # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
-extras["docs"] = ["recommonmark", "sphinx", "sphinx-markdown-tables", "sphinx-rtd-theme==0.4.3"]
+extras["docs"] = ["recommonmark", "sphinx", "sphinx-markdown-tables", "sphinx-rtd-theme==0.4.3", "sphinx-copybutton"]
 extras["quality"] = [
     "black",
     "isort @ git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort",

src/transformers/configuration_albert.py

@@ -81,22 +81,22 @@ class AlbertConfig(PretrainedConfig):
 
         Example::
 
-            from transformers import AlbertConfig, AlbertModel
-            # Initializing an ALBERT-xxlarge style configuration
-            albert_xxlarge_configuration = AlbertConfig()
-
-            # Initializing an ALBERT-base style configuration
-            albert_base_configuration = AlbertConfig(
-                hidden_size=768,
-                num_attention_heads=12,
-                intermediate_size=3072,
-            )
-
-            # Initializing a model from the ALBERT-base style configuration
-            model = AlbertModel(albert_xxlarge_configuration)
-
-            # Accessing the model configuration
-            configuration = model.config
+            >>> from transformers import AlbertConfig, AlbertModel
+            >>> # Initializing an ALBERT-xxlarge style configuration
+            >>> albert_xxlarge_configuration = AlbertConfig()
+
+            >>> # Initializing an ALBERT-base style configuration
+            >>> albert_base_configuration = AlbertConfig(
+            ...      hidden_size=768,
+            ...      num_attention_heads=12,
+            ...      intermediate_size=3072,
+            ...  )
+
+            >>> # Initializing a model from the ALBERT-base style configuration
+            >>> model = AlbertModel(albert_xxlarge_configuration)
+
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
     """
 
     model_type = "albert"

src/transformers/configuration_bart.py

@@ -73,9 +73,13 @@ class BartConfig(PretrainedConfig):
     ):
         r"""
             :class:`~transformers.BartConfig` is the configuration class for `BartModel`.
-            Examples:
-                config = BartConfig.from_pretrained('bart-large')
-                model = BartModel(config)
+
+            Examples::
+
+                >>> from transformers import BartConfig, BartModel
+
+                >>> config = BartConfig.from_pretrained('facebook/bart-large')
+                >>> model = BartModel(config)
         """
         if "hidden_size" in common_kwargs:
             raise ValueError("hidden size is called d_model")

src/transformers/configuration_bert.py

@@ -95,16 +95,16 @@ class BertConfig(PretrainedConfig):
 
         Example::
 
-            from transformers import BertModel, BertConfig
+            >>> from transformers import BertModel, BertConfig
 
-            # Initializing a BERT bert-base-uncased style configuration
-            configuration = BertConfig()
+            >>> # Initializing a BERT bert-base-uncased style configuration
+            >>> configuration = BertConfig()
 
-            # Initializing a model from the bert-base-uncased style configuration
-            model = BertModel(configuration)
+            >>> # Initializing a model from the bert-base-uncased style configuration
+            >>> model = BertModel(configuration)
 
-            # Accessing the model configuration
-            configuration = model.config
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
     """
     model_type = "bert"
 

src/transformers/configuration_ctrl.py

@@ -66,16 +66,16 @@ class CTRLConfig(PretrainedConfig):
 
         Example::
 
-            from transformers import CTRLModel, CTRLConfig
+            >>> from transformers import CTRLModel, CTRLConfig
 
-            # Initializing a CTRL configuration
-            configuration = CTRLConfig()
+            >>> # Initializing a CTRL configuration
+            >>> configuration = CTRLConfig()
 
-            # Initializing a model from the configuration
-            model = CTRLModel(configuration)
+            >>> # Initializing a model from the configuration
+            >>> model = CTRLModel(configuration)
 
-            # Accessing the model configuration
-            configuration = model.config
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
     """
 
     model_type = "ctrl"

src/transformers/configuration_distilbert.py

@@ -80,16 +80,16 @@ class DistilBertConfig(PretrainedConfig):
 
         Example::
 
-            from transformers import DistilBertModel, DistilBertConfig
+            >>> from transformers import DistilBertModel, DistilBertConfig
 
-            # Initializing a DistilBERT configuration
-            configuration = DistilBertConfig()
+            >>> # Initializing a DistilBERT configuration
+            >>> configuration = DistilBertConfig()
 
-            # Initializing a model from the configuration
-            model = DistilBertModel(configuration)
+            >>> # Initializing a model from the configuration
+            >>> model = DistilBertModel(configuration)
 
-            # Accessing the model configuration
-            configuration = model.config
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
     """
     model_type = "distilbert"
 

src/transformers/configuration_electra.py

@@ -101,16 +101,16 @@ class ElectraConfig(PretrainedConfig):
 
         Example::
 
-            from transformers import ElectraModel, ElectraConfig
+            >>> from transformers import ElectraModel, ElectraConfig
 
-            # Initializing a ELECTRA electra-base-uncased style configuration
-            configuration = ElectraConfig()
+            >>> # Initializing a ELECTRA electra-base-uncased style configuration
+            >>> configuration = ElectraConfig()
 
-            # Initializing a model from the electra-base-uncased style configuration
-            model = ElectraModel(configuration)
+            >>> # Initializing a model from the electra-base-uncased style configuration
+            >>> model = ElectraModel(configuration)
 
-            # Accessing the model configuration
-            configuration = model.config
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
     """
     model_type = "electra"
 

src/transformers/configuration_encoder_decoder.py

@@ -42,20 +42,20 @@ class EncoderDecoderConfig(PretrainedConfig):
 
         Example::
 
-            from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
+            >>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
 
-            # Initializing a BERT bert-base-uncased style configuration
-            config_encoder = BertConfig()
-            config_decoder = BertConfig()
+            >>> # Initializing a BERT bert-base-uncased style configuration
+            >>> config_encoder = BertConfig()
+            >>> config_decoder = BertConfig()
 
-            config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
+            >>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
 
-            # Initializing a Bert2Bert model from the bert-base-uncased style configurations
-            model = EncoderDecoderModel(config=config)
+            >>> # Initializing a Bert2Bert model from the bert-base-uncased style configurations
+            >>> model = EncoderDecoderModel(config=config)
 
-            # Accessing the model configuration
-            config_encoder = model.config.encoder
-            config_decoder  = model.config.decoder
+            >>> # Accessing the model configuration
+            >>> config_encoder = model.config.encoder
+            >>> config_decoder  = model.config.decoder
     """
     model_type = "encoder_decoder"
 

src/transformers/configuration_gpt2.py

@@ -100,16 +100,16 @@ class GPT2Config(PretrainedConfig):
 
         Example::
 
-            from transformers import GPT2Model, GPT2Config
+            >>> from transformers import GPT2Model, GPT2Config
 
-            # Initializing a GPT2 configuration
-            configuration = GPT2Config()
+            >>> # Initializing a GPT2 configuration
+            >>> configuration = GPT2Config()
 
-            # Initializing a model from the configuration
-            model = GPT2Model(configuration)
+            >>> # Initializing a model from the configuration
+            >>> model = GPT2Model(configuration)
 
-            # Accessing the model configuration
-            configuration = model.config
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
     """
 
     model_type = "gpt2"

src/transformers/configuration_longformer.py

@@ -49,16 +49,16 @@ class LongformerConfig(RobertaConfig):
 
         Example::
 
-            from transformers import LongformerConfig, LongformerModel
+            >>> from transformers import LongformerConfig, LongformerModel
 
-            # Initializing a Longformer configuration
-            configuration = LongformerConfig()
+            >>> # Initializing a Longformer configuration
+            >>> configuration = LongformerConfig()
 
-            # Initializing a model from the configuration
-            model = LongformerModel(configuration)
+            >>> # Initializing a model from the configuration
+            >>> model = LongformerModel(configuration)
 
-            # Accessing the model configuration
-            configuration = model.config
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
     """
     model_type = "longformer"
 

src/transformers/configuration_mobilebert.py

@@ -85,16 +85,16 @@ class MobileBertConfig(PretrainedConfig):
 
         Example:
 
-            from transformers import MobileBertModel, MobileBertConfig
+            >>> from transformers import MobileBertModel, MobileBertConfig
 
-            # Initializing a MobileBERT configuration
-            configuration = MobileBertConfig()
+            >>> # Initializing a MobileBERT configuration
+            >>> configuration = MobileBertConfig()
 
-            # Initializing a model from the configuration above
-            model = MobileBertModel(configuration)
+            >>> # Initializing a model from the configuration above
+            >>> model = MobileBertModel(configuration)
 
-            # Accessing the model configuration
-            configuration = model.config
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
 
         Attributes:
             pretrained_config_archive_map (Dict[str, str]):

src/transformers/configuration_openai.py

@@ -98,16 +98,16 @@ class OpenAIGPTConfig(PretrainedConfig):
 
         Example::
 
-            from transformers import OpenAIGPTConfig, OpenAIGPTModel
+            >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel
 
-            # Initializing a GPT configuration
-            configuration = OpenAIGPTConfig()
+            >>> # Initializing a GPT configuration
+            >>> configuration = OpenAIGPTConfig()
 
-            # Initializing a model from the configuration
-            model = OpenAIGPTModel(configuration)
+            >>> # Initializing a model from the configuration
+            >>> model = OpenAIGPTModel(configuration)
 
-            # Accessing the model configuration
-            configuration = model.config
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
     """
 
     model_type = "openai-gpt"