BERT is a transformer-based model designed for understanding the context of words in a sentence by processing text in both directions, which significantly improves the performance of various natural language processing tasks. It uses a masked language model approach to pre-train deep bidirectional representations, allowing it to capture the meaning of ambiguous language and the nuances of context more effectively than previous models.