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 variable length English text and the output is a 512 dimensional vector. We apply this model to the STS benchmark for semantic similarity, and the results can be seen in the example notebook made available. The universal-sentence-encoder model is trained with a deep averaging network (DAN) encoder.
The details are described in the paper “Universal Sentence Encoder”.
How to use
embeddings = UniversalSentenceEncoder.pretrained("tfhub_use_lg", "en") \ .setInputCols("document") \ .setOutputCol("sentence_embeddings")
val embeddings = UniversalSentenceEncoder.pretrained("tfhub_use_lg", "en") .setInputCols("document") .setOutputCol("sentence_embeddings")
|Compatibility:||Spark NLP 2.4.0|
The model is imported from https://tfhub.dev/google/universal-sentence-encoder-large/3