QuickStart code error
In the model2model quickstart example, I'm getting an error here:

`outputs = model(question_tensor, answer_tensor, decoder_lm_labels=labels_tensor)`

With the following message:

`RuntimeError: The size of tensor a (8) must match the size of tensor b (768) at non-singleton dimension 3`

Any ideas?
Hi! Indeed this was an error, it should have been fixed with https://github.com/huggingface/transformers/commit/90ab15cb7a8fcf8bf58c05453ddf1aa6a4fa00c1.

Could you try installing from source:

```py
pip install git+https://github.com/huggingface/transformers
```

and let me know if it fixes your issue?<|||||>Install was successful.

But now I get the following:

> Traceback (most recent call last):
>  File "model2model.py", line 82, in <module>
>     model = Model2Model.from_pretrained('fine-tuned-weights')
>   File "/path/venvs/nlp/lib/python3.6/site-packages/transformers/modeling_encoder_decoder.py", line 323, in from_pretrained
>    raise ValueError("Only the Bert model is currently supported.")
> ValueError: Only the Bert model is currently supported.

Traded on error for another.
<|||||>```py
model = Model2Model.from_pretrained('fine-tuned-weights')
```

Do you have a folder called `fine-tuned-weights` in your directory?<|||||>To wrap this up: Just to confirm, there is no existing 'fine-tuned-weights' pretrained model.

'fine-tuned-weights' is just a name for a hypothetical pretrained model.<|||||>Thank you, this commit works fine and fix the issue.<|||||>I'm still a bit confused going along with the Quickstart guide and trying to get a fine-tuned Model2Model to work. 
First of all, as a minor note, the suggested line in the guide
 `model = Model2Model.from_pretrained('fine-tuned-weights')`
won't work _even if a folder with that name exists_, as `from_pretrained` actually checks if this model path or name contains the string "bert" (among other things, see [here](https://github.com/huggingface/transformers/blob/e693cd1e877aa191d3317faed33e87d1558c9406/src/transformers/modeling_encoder_decoder.py#L282)). I understand that this is more of a placeholder name than anything else, but it might still be confusing. 

Then, let's assume I saved a fine-tuned Model2Model instance via `model.save_pretrained(PATH)` (where this PATH now contains the string "bert"). The suggested loading of this via `from_pretrained`will still fail: A saved Model2Model is actually split into encoder and decoder, so simply using the top directory containing both for loading will obviously fail. Thus, I only have the option of either loading the encoder _or_ decoder model, which will then, in the newly loaded Model2Model instance, be used as _both the encoder and decoder_, as this is how Model2Model is loaded: a single (BERT-)model used as encoder and decoder. But that can't be correct for _fine-tuned_ versions of this model, can it? Or am I just missing something obvious here? 



<|||||>Hi @redfarg, thanks for you comment. This is misleading indeed. We're in the process of adding BART to the library (@sshleifer), improving the experience with encoder-decoder architectures/Model2Model is part of the roadmap.