Bert: fine-tuning the entire pre-trained model end-to-end vs using contextual token vector
It goes like this: In the case of fine-tuning, you may want to use huge batch size, which may lead to the Out of Memory (OOM) issues. In such case, you may find the embeddings of BERT useful than fine-tuning on BERT itself. Maybe you can think of using BERT embeddings to train CNN or RNN classifier, in such case you can try to obtain the embeddings of BERT using a small batch size (it can be as low as 1), and then use these embeddings to further train your CNN or RNN classifier.