Loading pretrained RobertaForSequenceClassification fails, size missmatch error
## 🐛 Bug

<!-- Important information -->

Model I am using `RobertaForSequenceClassification` and when I tried to load `'roberta-base'` model using this code on Google Colab:

```from transformers import RobertaForSequenceClassification, RobertaConfig
config = RobertaConfig()
model = RobertaForSequenceClassification.from_pretrained(
    "roberta-base", config = config)
model
```

I get the following error:
```
RuntimeError: Error(s) in loading state_dict for RobertaForSequenceClassification:
	size mismatch for roberta.embeddings.word_embeddings.weight: copying a param with shape torch.Size([50265, 768]) from checkpoint, the shape in current model is torch.Size([30522, 768]).
	size mismatch for roberta.embeddings.position_embeddings.weight: copying a param with shape torch.Size([514, 768]) from checkpoint, the shape in current model is torch.Size([512, 768]).
	size mismatch for roberta.embeddings.token_type_embeddings.weight: copying a param with shape torch.Size([1, 768]) from checkpoint, the shape in current model is torch.Size([2, 768]).
```

Maybe related to #1340 

## Environment

* Google Colab Platform Linux-4.14.137+-x86_64-with-Ubuntu-18.04-bionic
Python 3.6.8 (default, Oct  7 2019, 12:59:55) 
[GCC 8.3.0]
PyTorch 1.3.0+cu100
Transformers 2.1.1


Hi! You're initializing RoBERTa with a blank configuration, which results in a very BERT-like configuration. BERT has different attributes than RoBERTa (different vocabulary size, positional embeddings size etc) so this indeed results in an error.

To instantiate RoBERTa you can simply do:

```py
model = RobertaForSequenceClassification.from_pretrained("roberta-base")
```

If you wish to have a configuration file so that you can change attributes like outputting the hidden states, you could do it like this:

```py
config = RobertaConfig.from_pretrained("roberta-base", output_hidden_states=True)
model = RobertaForSequenceClassification.from_pretrained("roberta-base", config=config)
```<|||||>Hi @LysandreJik ,

Thanks a lot for the clarification, this is indeed much clearer.  I tried the code again and it is working.