What does BERT add to the transformer architecture?

BERT (Bidirectional Encoder Representations from Transformers) adds several key features to the basic transformer architecture that make it effective for natural language processing:

• Bidirectionality: Unlike classic transformers that read text from left to right or right to left, BERT is trained to consider context from both sides simultaneously. This is achieved through masked language modeling, where some words in a sentence are hidden, and the model must predict them using context from both the left and right.

• Pre-training on large corpora: BERT is pre-trained on large amounts of text using Masked LM and Next Sentence Prediction tasks, allowing the model to capture deep language dependencies and semantics.

• Use of only the transformer encoder: BERT is based on the encoder architecture of the transformer without a decoder, suitable for understanding tasks.

Example of masked language modeling:

# Example of a masked sentence:
# "The [MASK] cat sat on the mat."
# The model should predict the word "black" or another appropriate word.

Thus, BERT extends the transformer architecture, making it bidirectional and suitable for deep understanding of context in natural language processing tasks.