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Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers:

<details><summary>List of implemented papers</summary><p> </p></details>

What's New:

<details><summary>Previous updates</summary><p> </p></details>

Features:

We also provide pre-trained models for translation and language modeling with a convenient torch.hub interface:

en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')
en2de.translate('Hello world', beam=5)
# 'Hallo Welt'

See the PyTorch Hub tutorials for translation and RoBERTa for more examples.

Requirements and Installation

git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./

# on MacOS:
# CFLAGS="-stdlib=libc++" pip install --editable ./
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
  --global-option="--deprecated_fused_adam" --global-option="--xentropy" \
  --global-option="--fast_multihead_attn" ./

Getting Started

The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks.

Pre-trained models and examples

We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, as well as example training and evaluation commands.

We also have more detailed READMEs to reproduce results from specific papers:

Join the fairseq community

License

fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well.

Citation

Please cite as:

@inproceedings{ott2019fairseq,
  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
  year = {2019},
}