BERT (Bidirectional Encoder Representations from Transformers)

BERT processes text bidirectionally, meaning it considers both the words before and after a particular word to understand its context. Built on the transformer architecture, BERT is pre-trained on large amounts of text and can then be fine-tuned for specific natural language processing tasks. This approach helps the model understand relationships, meaning, and context within human language.

BERT significantly improved how AI systems understand contextual language, particularly when words have different meanings depending on their surroundings. Its pre-training and fine-tuning approach also allows developers to build effective NLP applications without training language models entirely from scratch.