The Universal Sentence Encoder (USE) is a model that encodes sentences into embedding vectors for transfer learning in natural language processing (NLP) tasks. The model is efficient and achieves accurate performance on various transfer tasks. Two variants of the encoding models are presented: one based on the transformer architecture and another based on a deep averaging network (DAN). Both models are implemented in TensorFlow and are available on TF Hub. The transformer-based model achieves high accuracy but requires more computational resources, while the DAN model is more efficient but slightly less accurate. Both models are trained on a variety of web sources and the Stanford Natural Language Inference (SNLI) corpus. The models are evaluated on several transfer tasks, including sentiment analysis, question classification, and semantic textual similarity (STS). The results show that sentence-level transfer learning outperforms word-level transfer learning. The USE models are also evaluated on the Word Embedding Association Test (WEAT), which measures model bias. The results show that the models reproduce human associations between certain concepts but demonstrate weaker associations for probes targeting ageism, racism, and sexism. The models make different trade-offs between accuracy and computational efficiency, which should be considered when choosing the best model for a particular application. The models are publicly available for research and application. Transfer learning is particularly useful when training data is limited, and the models demonstrate strong performance on a variety of NLP tasks. The models are efficient and can be used for a wide range of applications that benefit from a better understanding of natural language.The Universal Sentence Encoder (USE) is a model that encodes sentences into embedding vectors for transfer learning in natural language processing (NLP) tasks. The model is efficient and achieves accurate performance on various transfer tasks. Two variants of the encoding models are presented: one based on the transformer architecture and another based on a deep averaging network (DAN). Both models are implemented in TensorFlow and are available on TF Hub. The transformer-based model achieves high accuracy but requires more computational resources, while the DAN model is more efficient but slightly less accurate. Both models are trained on a variety of web sources and the Stanford Natural Language Inference (SNLI) corpus. The models are evaluated on several transfer tasks, including sentiment analysis, question classification, and semantic textual similarity (STS). The results show that sentence-level transfer learning outperforms word-level transfer learning. The USE models are also evaluated on the Word Embedding Association Test (WEAT), which measures model bias. The results show that the models reproduce human associations between certain concepts but demonstrate weaker associations for probes targeting ageism, racism, and sexism. The models make different trade-offs between accuracy and computational efficiency, which should be considered when choosing the best model for a particular application. The models are publicly available for research and application. Transfer learning is particularly useful when training data is limited, and the models demonstrate strong performance on a variety of NLP tasks. The models are efficient and can be used for a wide range of applications that benefit from a better understanding of natural language.