Skip to content

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

License

Notifications You must be signed in to change notification settings

mt-upc/fairseq

Folders and files

NameName
Last commit message
Last commit date

Latest commit

c3ea27f · Oct 30, 2022
Dec 9, 2021
Nov 29, 2021
Sep 20, 2021
Feb 1, 2022
Oct 30, 2022
Feb 2, 2022
Dec 9, 2021
Jan 27, 2022
Jan 20, 2022
Nov 16, 2020
Nov 25, 2021
Dec 9, 2021
Mar 4, 2020
Nov 25, 2021
Jul 30, 2019
Dec 28, 2021
Jan 5, 2021
Feb 16, 2020
Dec 9, 2021
Nov 29, 2021
Oct 19, 2020

Repository files navigation



MIT License Latest Release Build Status Documentation Status


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:

List of implemented papers

What's New:

Previous updates

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

  • PyTorch version >= 1.5.0
  • Python version >= 3.6
  • For training new models, you'll also need an NVIDIA GPU and NCCL
  • To install fairseq and develop locally:
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./

# on MacOS:
# CFLAGS="-stdlib=libc++" pip install --editable ./

# to install the latest stable release (0.10.x)
# pip install fairseq
  • For faster training install NVIDIA's apex library:
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" ./
  • For large datasets install PyArrow: pip install pyarrow
  • If you use Docker make sure to increase the shared memory size either with --ipc=host or --shm-size as command line options to nvidia-docker run .

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},
}

About

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Resources

License

Code of conduct

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 98.2%
  • Other 1.8%