diff --git a/src/transformers/configuration_encoder_decoder.py b/src/transformers/configuration_encoder_decoder.py
index 0b9873f910..2fafbebb8d 100644
--- a/src/transformers/configuration_encoder_decoder.py
+++ b/src/transformers/configuration_encoder_decoder.py
@@ -27,18 +27,35 @@ class EncoderDecoderConfig(PretrainedConfig):
     r"""
         :class:`~transformers.EncoderDecoderConfig` is the configuration class to store the configuration of a `EncoderDecoderModel`.
 
-            It is used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder configs.
-            Configuration objects inherit from  :class:`~transformers.PretrainedConfig`
-            and can be used to control the model outputs.
-            See the documentation for :class:`~transformers.PretrainedConfig` for more information.
+        It is used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder configs.
+        Configuration objects inherit from  :class:`~transformers.PretrainedConfig`
+        and can be used to control the model outputs.
+        See the documentation for :class:`~transformers.PretrainedConfig` for more information.
 
+        Args:
+            kwargs (`optional`):
+                Remaining dictionary of keyword arguments. Notably:
+                    encoder (:class:`PretrainedConfig`, optional, defaults to `None`):
+                        An instance of a configuration object that defines the encoder config.
+                    encoder (:class:`PretrainedConfig`, optional, defaults to `None`):
+                        An instance of a configuration object that defines the decoder config.
 
-        Arguments:
-            kwargs: (`optional`) Remaining dictionary of keyword arguments. Notably:
-                encoder (:class:`PretrainedConfig`, optional, defaults to `None`):
-                    An instance of a configuration object that defines the encoder config.
-                encoder (:class:`PretrainedConfig`, optional, defaults to `None`):
-                    An instance of a configuration object that defines the decoder config.
+        Example::
+
+            from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
+
+            # Initializing a BERT bert-base-uncased style configuration
+            config_encoder = BertConfig()
+            config_decoder = BertConfig()
+
+            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)
+
+            # Accessing the model configuration
+            config_encoder = model.config.encoder
+            config_decoder  = model.config.decoder
     """
     model_type = "encoder_decoder"
 
diff --git a/src/transformers/modeling_encoder_decoder.py b/src/transformers/modeling_encoder_decoder.py
index 02b95c7baa..451edc6c03 100644
--- a/src/transformers/modeling_encoder_decoder.py
+++ b/src/transformers/modeling_encoder_decoder.py
@@ -125,6 +125,8 @@ class EncoderDecoderModel(PreTrainedModel):
 
         Examples::
 
+            from tranformers import EncoderDecoder
+
             model = EncoderDecoder.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert
         """
 
@@ -230,6 +232,25 @@ class EncoderDecoderModel(PreTrainedModel):
             kwargs: (`optional`) Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
                 - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.
                 - With a `decoder_` prefix which will be input as `**decoder_kwargs` for the decoder forward function.
+
+        Examples::
+
+            from transformers import EncoderDecoderModel, BertTokenizer
+            import torch
+
+            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert
+
+            # forward
+            input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+            outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
+
+            # training
+            loss, outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, lm_labels=input_ids)[:2]
+
+            # generation
+            generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)
+
         """
 
         kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}