Roberta Classification Head
Why does Roberta classification Head uses 2 linear layers? https://github.com/huggingface/transformers/blob/84ea427f460ffc8d2ddc08a341ccda076c24fc1f/src/transformers/models/roberta/modeling_roberta.py#L1443 

The BERT classification head uses only one and it makes sense since all the work is done by the Transformer layers. 
Hi! We try to stick as much as possible to the original implementation. See fairseq's original implementation here: https://github.com/pytorch/fairseq/blob/a54021305d6b3c4c5959ac9395135f63202db8f1/fairseq/models/roberta/model.py#L394-L429<|||||>Thank you so much. 