A transformer is a machine learning model used in natural language processing that utilizes a neural network architecture to process and understand large amounts of text data. The proliferation of NLP has been propelled by the exceptional performance of Transformer-style networks such as BERT and GPT. The transformer uses an encoder-decoder structure to encode the input sequence into a vector, which is then decoded into the target sequence. This allows for deep understanding of complex language patterns and context within text. Transformers are commonly used in machine translation, sentiment analysis, question answering, speech recognition, and other text-based tasks. They are also becoming increasingly popular in other machine-learning fields such as image captioning and object detection. This is because they allow for the representation of more complex relationships between features and target outputs. Transformers are an invaluable tool in machine learning that enables us to make sense of huge amounts of text data. They are increasingly being used to create improvements in machine translation, natural language understanding, and machine learning tasks. 

Using Cerebras Systems’ CS-2, researchers and scientists can now rapidly train Transformer-style natural language AI models with 20x longer sequences than is possible using traditional computer hardware. Training large models with massive data sets and long sequence lengths is an area that the Cerebras CS-2 system, powered by the Wafer-Scale Engine (WSE-2), excels. This new capability is expected to lead to breakthroughs in natural language processing (NLP). By providing vastly more context to the understanding of a given word, phrase or strand of DNA, the long sequence length capability enables NLP models a much finer-grained understanding and better predictive accuracy.