Tips + whitespaces

src/transformers/modeling_tf_roberta.py

@@ -21,7 +21,7 @@ import logging
 import tensorflow as tf
 
 from .configuration_roberta import RobertaConfig
-from .file_utils import add_start_docstrings
+from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
 from .modeling_tf_bert import TFBertEmbeddings, TFBertMainLayer, gelu
 from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
 
@@ -184,6 +184,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
         super().__init__(config, *inputs, **kwargs)
         self.roberta = TFRobertaMainLayer(config, name="roberta")
 
+    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
     def call(self, inputs, **kwargs):
         r"""
     Returns:
@@ -266,6 +267,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head.decoder
 
+    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
     def call(self, inputs, **kwargs):
         r"""
     Return:
@@ -343,6 +345,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
         self.roberta = TFRobertaMainLayer(config, name="roberta")
         self.classifier = TFRobertaClassificationHead(config, name="classifier")
 
+    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
     def call(self, inputs, **kwargs):
         r"""
     Return:
@@ -399,6 +402,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel):
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
 
+    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
     def call(self, inputs, **kwargs):
         r"""
     Return:

src/transformers/modeling_xlm.py

@@ -251,6 +251,10 @@ class XLMPreTrainedModel(PreTrainedModel):
 
 XLM_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.XLMConfig`): Model configuration class with all the parameters of the model.
             Initializing with a config file does not load the weights associated with the model, only the configuration.

src/transformers/modeling_xlm_roberta.py

@@ -42,8 +42,10 @@ XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
 
 
 XLM_ROBERTA_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.XLMRobertaConfig`): Model configuration class with all the parameters of the

src/transformers/modeling_xlnet.py

@@ -506,6 +506,7 @@ class XLNetPreTrainedModel(PreTrainedModel):
 
 
 XLNET_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.