Fairseq is a sequence modeling toolkit for training custom models for translation, summarization, and other text generation tasks. It provides reference implementations of various sequence-to-sequence models, including Long Short-Term Memory (LSTM) networks and a novel convolutional neural network (CNN) that can generate translations many times faster than comparable recurrent neural network (RNN) models.

Better machine translations

Fairseq can train models that achieve state-of-the-art performance on machine translation and summarization tasks, and includes pre-trained models for several benchmark translation datasets.

Fairseq also features multi-GPU training on one or across multiple machines, and lightning fast beam search generation on both CPU and GGPU.

Get Started


  • Install fairseq-py.

    git clone https://github.com/pytorch/fairseq.git
    cd fairseq
    pip install -r requirements.txt
    python setup.py build develop

  • 3

  • Download a pre-trained model to familarize yourself with fairseq-py.

  • 4

  • Train a new model.

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