Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based machine learning technique for natural language processing developed by Google. BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of NLP tasks.