Universal sentence encoder
Note
|
Disclaimer Please note that datasets, machine-learning models, weights, topologies, research papers and other content, including open source software, (collectively referred to as “Content”) provided and/or suggested by Peltarion for use in the Platform and otherwise, may be subject to separate third party terms of use or license terms. You are solely responsible for complying with the applicable terms. Peltarion makes no representations or warranties about Content. You expressly relieve us from any and all liability, loss or risk arising (directly or indirectly) from Your use of any third party content. |
License
The universal-sentence-encoder-multilingual model with weights is released by Google, under the Apache License, Version 2.0.
The weights are pre-trained by Google on the Stanford Natural Language Inference (SNLI) corpus, licensed under CC BY-SA 4.0.
References
-
Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego , Steve Yuan, Chris Tar, Yun-hsuan Sung, Ray Kurzweil. Multilingual Universal Sentence Encoder for Semantic Retrieval. July 2019
-
Muthuraman Chidambaram, Yinfei Yang, Daniel Cer, Steve Yuan, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil. Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model. To appear, Repl4NLP@ACL, July 2019.
-
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP).