This model is a full precision version of the original universal-sentence-encoder, wrapped by input and output reshaping layers.

These wrapping processing layers change the input signature from Rank-1 (Lists) to Rank-2 (Matrices), making the model compatible with the serving environments of the Actian Analytics Engine that requires an input dimension of Rank-2 or higher.

The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.

The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs. It is trained on a variety of data sources and a variety of tasks with the aim of dynamically accommodating a wide variety of natural language understanding tasks. The input is column of English strings and the output is a column of 512 dimensional vectors.