Transformer

Transformers are deep learning models that use self-attention to weigh the importance of different parts of input data. They are used mainly in natural language processing (NLP) and computer vision. Unlike recurrent neural networks, transformers process the entire input at once, allowing for more parallelization and faster training times. Transformers have become the model of choice for NLP problems, replacing RNNs such as LSTM, and have led to the development of pre-trained systems such as BERT and GPT, which can be fine-tuned for specific tasks.