title: Can you train a BERT model from scratch with task specific architecture?
body: <p>BERT pre-training of the base-model is done by a language modeling approach, where we mask certain percent of tokens in a sentence, and we make the model learn those missing mask. Then, I think in order to do downstream tasks, we add a newly initialized layer and we fine-tune the model.</p>
  
  <p>However, suppose we have a gigantic dataset for sentence classification. Theoretically, can we initialize the BERT base architecture from scratch, train both the additional downstream task specific layer + the base model weights form scratch with this sentence classification dataset only, and still achieve a good result?</p>
  
  <p>Thanks.</p>