DPR with ELECTRA models
# 🚀 Feature request

Dense Passage Retrieval should work with any language model as encoder, in particular also with ELECTRA models.
However, the current implementation of the class `DPREncoder` seems to be limited to BERT models:

https://github.com/huggingface/transformers/blob/a5d2967bd8a5ed2456c593fa9eb5d9c0d726ae7a/src/transformers/models/dpr/modeling_dpr.py#L155

In my understanding, ELECTRA does not provide a pooled_output by default. Therefore, the following line breaks because there are not enough values to unpack (expected 2, got 1):

https://github.com/huggingface/transformers/blob/a5d2967bd8a5ed2456c593fa9eb5d9c0d726ae7a/src/transformers/models/dpr/modeling_dpr.py#L181

## Motivation
I would like to train a Dense Passage Retrieval model with transformers and FARM. It works with BERT models as encoders of queries and passages but not with ELECTRA models. I expect the ELECTRA DPR model to outperform the BERT DPR model with regard to retrieval performance. The code for training DPR is similar to this example [here](https://github.com/deepset-ai/haystack/blob/master/tutorials/Tutorial9_DPR_training.py).

If the handling of the pooled_output is really the only reason that prevents ELECTRA models from being used, I could imagine to work on this myself but I'd be happy to get some guidance first. Maybe there are other problems that I oversaw.

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