Roberta does not have a pooler layer (like Bert for instance) since the pretraining objective does not contain a classification task. When doing sentence classification with bert, your final hidden states go through a BertPooler (which is just dense + tanh), a dropout and a final classification layer (which is a dense layer).
This structure is mimicked for all models on a sentence classification task, which is why for Roberta (which does not have a pooler) you get those two linear layers in the classification head. Hope that makes sense!