BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking NLP model from Google that learns deep, bidirectional text representations using a transformer architecture. This allows for a richer contextual understanding than previous models that only processed text unidirectionally. BERT is pre-trained using a masked language model and a next sentence prediction task on large amounts of unlabeled text. The pre-trained model can be fine-tuned for various tasks such as question answering, language inference, and text classification. It has achieved state-of-the-art results on many NLP tasks.