TF XLM

src/transformers/modeling_tf_xlnet.py

@@ -696,22 +696,24 @@ class TFXLNetPreTrainedModel(TFPreTrainedModel):
 
 XLNET_START_DOCSTRING = r"""    
 
-    .. note:
+    .. note::
     
         TF 2.0 models accepts two formats as inputs:
 
             - having all inputs as keyword arguments (like PyTorch models), or
             - having all inputs as a list, tuple or dict in the first positional arguments.
 
-        This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
+        This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having 
+        all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
 
-        If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
+        If you choose this second option, there are three possibilities you can use to gather all the input Tensors 
+        in the first positional argument :
 
-        - a single Tensor with input_ids only and nothing else: `model(inputs_ids)
+        - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
         - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
-            `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
-        - a dictionary with one or several input Tensors associaed to the input names given in the docstring:
-            `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
+          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
+        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+          :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
 
     Parameters:
         config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model.