Example for T5 model from doc is not working.
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 2.3.0
- Platform: Linux
- Python version: 3.6
- PyTorch version (GPU?): No
- Tensorflow version (GPU?): No
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
@patrickvonplaten

 
## Information

Model I am using T5 (TFT5) and following the [documentation](https://huggingface.co/transformers/model_doc/t5.html):

The problem arises when using:
* the official example scripts: (give details below)



## To reproduce

Steps to reproduce the behavior:

```
import tensorflow as tf
from transformers import TFT5Model
model = TFT5Model.from_pretrained('t5-small')
inputs = tf.constant([[ 1, 30,  4, 19,  7, 41, 20,  4, 25, 40, 13, 46, 27, 54, 25,  2]])
print(model(inputs, labels=inputs))
```

The above snippet throws the error 
```
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-98-0a790979b424> in <module>()
----> 1 model(tf.constant([[ 1, 30,  4, 19,  7, 41, 20,  4, 25, 40, 13, 46, 27, 54, 25,  2]]))

3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    983 
    984         with ops.enable_auto_cast_variables(self._compute_dtype_object):
--> 985           outputs = call_fn(inputs, *args, **kwargs)
    986 
    987         if self._activity_regularizer:

/usr/local/lib/python3.6/dist-packages/transformers/modeling_tf_t5.py in call(self, inputs, attention_mask, encoder_outputs, inputs_embeds, head_mask, past_key_values, decoder_input_ids, decoder_attention_mask, decoder_inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, training, **kwargs)
   1104                 output_hidden_states,
   1105             ],
-> 1106             training=training,
   1107         )
   1108         past = (

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    983 
    984         with ops.enable_auto_cast_variables(self._compute_dtype_object):
--> 985           outputs = call_fn(inputs, *args, **kwargs)
    986 
    987         if self._activity_regularizer:

/usr/local/lib/python3.6/dist-packages/transformers/modeling_tf_t5.py in call(self, inputs, attention_mask, encoder_hidden_states, encoder_attention_mask, inputs_embeds, head_mask, past_key_values, use_cache, output_attentions, output_hidden_states, training, **kwargs)
    646             input_shape = shape_list(inputs_embeds)[:-1]
    647         else:
--> 648             raise ValueError("You have to specify either inputs or inputs_embeds")
    649 
    650         if inputs_embeds is None:

ValueError: You have to specify either inputs or inputs_embeds
```
## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
This should instead return the instance of `TFSeq2SeqModelOutput`
Hi, you're using the wrong model. `labels` cannot work with the `TFT5Model` as that's just the base model. You're probably looking for `TFT5ForConditionalGeneration`, which is the T5 base model with a language modeling head:

```py
import tensorflow as tf
from transformers import TFT5ForConditionalGeneration
model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
inputs = tf.constant([[ 1, 30,  4, 19,  7, 41, 20,  4, 25, 40, 13, 46, 27, 54, 25,  2]])
print(model(inputs, labels=inputs))
```

outputs:
```
(<tf.Tensor: shape=(16,), dtype=float32, numpy=
array([7.5659113 , 4.1611323 , 0.7870086 , 0.19761924, 0.10837179,
       1.0610694 , 0.53702176, 0.01974043, 0.1657649 , 0.07946267,
       0.19164713, 0.10359508, 0.844127  , 0.04230493, 0.0681754 ,
       0.1965235 ], dtype=float32)>, <tf.Tensor: shape=(1, 16, 32128), dtype=float32, numpy=
array([[[-14.546758 ,  -7.1824822
[...]
```<|||||>I want to train a model for language translation would TFT5ForConditionalGeneration work?<|||||>Even this case is also not working.
```
model = TFT5Model.from_pretrained('t5-small')
model(tf.constant([[ 1, 30,  4, 19,  7, 41, 20,  4, 25, 40, 13, 46, 27, 54, 25,  2]]))
```
which is throwing the same error as stated above.<|||||>Indeed, the error message is wrong here but as T5 is a seq2seq model it requires both `input_ids` and `decoder_input_ids`. We should update the docstrings/error message, but you can have more information [here, in the docs](https://huggingface.co/transformers/model_doc/t5.html#tft5model).

cc @patrickvonplaten <|||||>Gotcha! Thanks for clarifying that.