Facebook now uses Caffe2 deep learning for the site’s 4.5 billion daily translations

Live Facebook traffic in parts of Asia and Africa in July 2017 in a photo taken at Facebook headquarters in Menlo Park, California.

Image Credit: Khari Johnson / VentureBeat

Facebook announced today that it has started using neural network systems to carry out more than 4.5 billion translations that occur each day on the backend of the social network. Translations carried out with recurrent neural networks (RNNs) were able to scale with the use of Caffe2, a deep learning framework open-sourced by Facebook in April.

The Caffe2 team today also announced that in part due to work done around translation, the framework is now able to work with recurrent neural networks.

“Using Caffe2, we significantly improved the efficiency and quality of machine translation systems at Facebook. We got an efficiency boost of 2.5x, which allows us to deploy neural machine translation models into production,” the Caffe2 team said in a blog post. “As a result, all machine translation models at Facebook have been transitioned from phrase-based systems to neural models for all languages.”