Facebook NMT: Setting up a Neural Machine Translation System Training Course

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Course Code



7 hours (usually 1 day including breaks)


  • Some programming experience is helpful
  • Basic understanding of neural networks
  • Experience using the command line


  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions


In this instructor-led, live training, participants will learn how to use Facebook NMT (Fairseq) to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Format of the course

  • Part lecture, part discussion, heavy hands-on practice


  • If you wish to use specific source and target language content, please contact us to arrange.

Course Outline


  • Why Neural Machine Translation?
  • Borrowing from image recognition techniques

Overview of the Torch and Caffe2 projects

Overview of a Convolutional Neural Machine Translation model

  • Convolutional Sequence to Sequence Learning
  • Convolutional Encoder Model for Neural Machine Translation
  • Standard LSTM-based model

Overview of training approaches

  • About GPUs and CPUs
  • Fast beam search generation

Installation and setup

Evaluating pre-trained models

Preprocessing your data

Training the model


Converting a trained model to use CPU-only operations

Joining to the community

Closing remarks

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