Chapter 04.03: The Architecture
The architecture of BERT primarily revolves around transformer encoders, where stacked layers of self-attention mechanisms and feedforward neural networks are employed to generate contextualized representations of tokens in bidirectional context. During pre-training, BERT utilizes masked language modeling (MLM) and next sentence prediction (NSP) tasks to fine-tune the parameters of the transformer encoder layers, enabling the model to effectively capture semantic relationships and contextual nuances in text data.