Universal sentence encoder models encode textual data into high-dimensional vectors which can be used for various NLP tasks. It was introduced by Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope and Ray Kurzweil (researchers at Google Research) in April 2018. The encoders used in such models require modelling the meaning of word sequences instead of individual words. Apart from single words, the models are trained and optimized for text having more-than-word lengths such as sentences, phrases or paragraphs. There are two main variations of the model encoders coded in TensorFlow – one of them uses transformer architecture while the other is a deep averaging network (DAN).