BERT (Bidirectional Encoder Representations from Transformers) uses a transformer-based architecture for deep learning. The transformer architecture was introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017.

  The transformer architecture has become popular in the field of natural language processing (NLP) due to its ability to capture long-range dependencies in sequences efficiently. It employs self-attention mechanisms to calculate contextualized representations of words or tokens in a sentence.

  In the case of BERT, the model consists of multiple layers of transformer encoders, where each encoder layer contains multiple self-attention and feed-forward neural networks. BERT is pretrained on a large corpus by predicting masked words and sentence relationships, which helps it learn general language knowledge before being fine-tuned on specific downstream tasks.

  By using transformers, BERT can effectively model relationships and dependencies between words in both forward and backward directions, hence the name "bidirectional." This enables BERT to capture a deeper understanding of context within sentences, leading to improved performance in various NLP tasks like text classification, named entity recognition, and question-answering, among others.

  Overall, BERT's use of the transformer architecture allows it to handle complex contextual relationships and capture the semantics of natural language effectively.