Fixes

docs/source/model_doc/camembert.rst

@@ -10,49 +10,49 @@ It is a model trained on 138GB of French text.
 This implementation is the same as RoBERTa.
 
 ``CamembertConfig``
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertConfig
     :members:
 
 
 ``CamembertTokenizer``
-~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertTokenizer
     :members:
 
 
 ``CamembertModel``
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertModel
     :members:
 
 
 ``CamembertForMaskedLM``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForMaskedLM
     :members:
 
 
 ``CamembertForSequenceClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForSequenceClassification
     :members:
 
 
 ``CamembertForMultipleChoice``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForMultipleChoice
     :members:
 
 
 ``CamembertForTokenClassification``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.CamembertForTokenClassification
     :members:

docs/source/model_doc/ctrl.rst

 CTRL
 ----------------------------------------------------
 
-CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation`_
+CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_
 by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
 It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
 corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
 
-This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
-refer to the PyTorch documentation for all matter related to general usage and behavior.
+This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
+Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
+usage and behavior.
 
 Note: if you fine-tune a CTRL model using the Salesforce code (https://github.com/salesforce/ctrl),
 you'll be able to convert from TF to our HuggingFace/Transformers format using the 

src/transformers/modeling_albert.py

@@ -494,7 +494,7 @@ class AlbertModel(AlbertPreTrainedModel):
     ):
         r"""
     Return:
-        :obj:`Tuple` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
         last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the output of the last layer of the model.
         pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
@@ -726,7 +726,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
             If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
         loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
             Classification (or regression if config.num_labels==1) loss.
         logits ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
@@ -824,7 +824,7 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel):
             Position outside of the sequence are not taken into account for computing the loss.
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
         loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
             Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
         start_scores ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``

src/transformers/modeling_bert.py

@@ -546,8 +546,9 @@ class BertPreTrainedModel(PreTrainedModel):
 
 
 BERT_START_DOCSTRING = r"""    
-    This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
-    refer to the PyTorch documentation for all matter related to general usage and behavior.
+    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. 
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general 
+    usage and behavior.
 
     Parameters:
         config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
@@ -660,7 +661,7 @@ class BertModel(BertPreTrainedModel):
     ):
         r"""
     Return:
-        :obj:`Tuple` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the output of the last layer of the model.
         pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
@@ -1218,7 +1219,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
             of the input tensors. (see `input_ids` above)
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided):
             Classification loss.
         classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
@@ -1313,7 +1314,7 @@ class BertForTokenClassification(BertPreTrainedModel):
             Indices should be in ``[0, ..., config.num_labels - 1]``.
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
             Classification loss.
         scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
@@ -1409,7 +1410,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
             Position outside of the sequence are not taken into account for computing the loss.
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
             Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
         start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):

src/transformers/modeling_ctrl.py

@@ -291,7 +291,7 @@ class CTRLModel(CTRLPreTrainedModel):
     ):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`CTRLConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
         last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
@@ -478,7 +478,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
             computed for labels in ``[0, ..., config.vocab_size]``
 
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.CTRLConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
             Language modeling loss.
         prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):

src/transformers/modeling_distilbert.py

@@ -351,6 +351,9 @@ class DistilBertPreTrainedModel(PreTrainedModel):
 
 
 DISTILBERT_START_DOCSTRING = r"""
+    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. 
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general 
+    usage and behavior.
     
     Parameters:
         config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
@@ -416,7 +419,7 @@ class DistilBertModel(DistilBertPreTrainedModel):
     def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None):
         r"""
     Return:
-        :obj:`Tuple` comprising various elements depending on the configuration (:class:`~transformers.DistilBertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.DistilBertConfig`) and inputs:
         last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the output of the last layer of the model.
         hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
@@ -513,7 +516,7 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
             in ``[0, ..., config.vocab_size]``
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.DistilBertConfig`) and inputs:
         loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
             Masked language modeling loss.
         prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
@@ -585,7 +588,7 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
             If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.DistilBertConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
             Classification (or regression if config.num_labels==1) loss.
         logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
@@ -672,7 +675,7 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
             Position outside of the sequence are not taken into account for computing the loss.
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.DistilBertConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
             Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
         start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
@@ -758,7 +761,7 @@ class DistilBertForTokenClassification(DistilBertPreTrainedModel):
             Indices should be in ``[0, ..., config.num_labels - 1]``.
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.DistilBertConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
             Classification loss.
         scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)

src/transformers/modeling_gpt2.py

@@ -266,8 +266,9 @@ class GPT2PreTrainedModel(PreTrainedModel):
 
 
 GPT2_START_DOCSTRING = r"""    
-    This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
-    refer to the PyTorch documentation for all matter related to general usage and behavior.
+    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. 
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general 
+    usage and behavior.
 
     Parameters:
         config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model.
@@ -362,7 +363,7 @@ class GPT2Model(GPT2PreTrainedModel):
     ):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
         last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
@@ -547,7 +548,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
             computed for labels in ``[0, ..., config.vocab_size]``
 
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
             Language modeling loss.
         prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
@@ -658,7 +659,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
             of the input tensors. (see `input_ids` above)
 
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
         lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``lm_labels`` is provided):
             Language modeling loss.
         mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`multiple_choice_labels` is provided):

src/transformers/modeling_openai.py

@@ -375,7 +375,7 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
     ):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
         last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
@@ -522,7 +522,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
             computed for labels in ``[0, ..., config.vocab_size]``
 
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.OpenAIGPTConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
             Language modeling loss.
         prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
@@ -627,7 +627,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
             of the input tensors. (see `input_ids` above)
 
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.OpenAIGPTConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
         lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``lm_labels`` is provided):
             Language modeling loss.
         mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`multiple_choice_labels` is provided):

src/transformers/modeling_roberta.py

@@ -401,7 +401,7 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
             of the input tensors. (see `input_ids` above)
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
         loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided):
             Classification loss.
         classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
@@ -497,7 +497,7 @@ class RobertaForTokenClassification(BertPreTrainedModel):
             Indices should be in ``[0, ..., config.num_labels - 1]``.
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.RobertaConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
             Classification loss.
         scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
@@ -616,7 +616,7 @@ class RobertaForQuestionAnswering(BertPreTrainedModel):
             Position outside of the sequence are not taken into account for computing the loss.
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
             Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
         start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):

src/transformers/modeling_tf_albert.py

@@ -719,7 +719,7 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
         prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -776,7 +776,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.AlbertConfig`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
         logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`)
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):

src/transformers/modeling_tf_bert.py

@@ -668,7 +668,7 @@ class TFBertModel(TFBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
         Returns:
-            :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
             last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
                 Sequence of hidden-states at the output of the last layer of the model.
             pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
@@ -724,7 +724,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         seq_relationship_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`):
@@ -780,7 +780,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -830,7 +830,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         seq_relationship_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`)
             Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -886,7 +886,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -952,7 +952,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
     ):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         classification_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`:
             `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
 
@@ -1054,7 +1054,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
             Classification scores (before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -1111,7 +1111,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
             Span-start scores (before SoftMax).
         end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):

src/transformers/modeling_tf_ctrl.py

@@ -436,7 +436,7 @@ class TFCTRLModel(TFCTRLPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
         last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
@@ -509,7 +509,7 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
         prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):

src/transformers/modeling_tf_distilbert.py

@@ -544,7 +544,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
         last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the output of the last layer of the model.
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -618,7 +618,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
         r"""
 
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
         prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -682,7 +682,7 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
         logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -740,7 +740,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
         scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
             Classification scores (before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -795,7 +795,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
         start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
             Span-start scores (before SoftMax).
         end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):

src/transformers/modeling_tf_gpt2.py

@@ -452,7 +452,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
         last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
@@ -504,7 +504,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
         prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
@@ -585,7 +585,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
             Selected in the range ``[0, input_ids.size(-1) - 1[``.
 
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
         lm_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         mc_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):

src/transformers/modeling_tf_openai.py

@@ -435,7 +435,7 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
         last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         hidden_states (:obj:`tuple(tf.Tensor)` `optional`, returned when ``config.output_hidden_states=True``):
@@ -483,7 +483,7 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
         prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
@@ -558,7 +558,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
             Selected in the range ``[0, input_ids.size(-1) - 1[``.
 
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
         lm_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         mc_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):

src/transformers/modeling_tf_roberta.py

@@ -187,7 +187,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
         last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the output of the last layer of the model.
         pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
@@ -269,7 +269,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
         prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -346,7 +346,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
         logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -402,7 +402,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
         scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
             Classification scores (before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):

src/transformers/modeling_tf_transfo_xl.py

@@ -695,7 +695,7 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
         last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         mems (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
@@ -759,7 +759,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
     def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, labels=None, training=False):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
         prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         mems (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):

src/transformers/modeling_tf_xlm.py

@@ -579,7 +579,7 @@ class TFXLMModel(TFXLMPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
         last_hidden_state (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the output of the last layer of the model.
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
@@ -661,7 +661,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
         prediction_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
@@ -714,7 +714,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
         logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
         hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
@@ -768,7 +768,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
         start_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
             Span-start scores (before SoftMax).
         end_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):

src/transformers/modeling_tf_xlnet.py

@@ -788,7 +788,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
         last_hidden_state (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
@@ -841,7 +841,7 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
         prediction_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
@@ -910,7 +910,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        `tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
         logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
         mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
@@ -970,7 +970,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Return:
-        `tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
         logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:(batch_size, config.num_labels)`):
             Classification scores (before SoftMax).
         mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
@@ -1028,7 +1028,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel):
     def call(self, inputs, **kwargs):
         r"""
     Returns:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
         loss (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
             Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
         start_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):

src/transformers/modeling_transfo_xl.py

@@ -673,7 +673,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
     def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None):
         r"""
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
         last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
             Sequence of hidden-states at the last layer of the model.
         mems (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
@@ -860,7 +860,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
             computed for labels in ``[0, ..., config.vocab_size]``
 
     Return:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
             Language modeling loss.
         prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):