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Myle Ott b002d0096e | 4 years ago | |
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docs | 4 years ago | |
examples | 4 years ago | |
fairseq | 4 years ago | |
fairseq_cli | 5 years ago | |
scripts | 4 years ago | |
tests | 4 years ago | |
.gitignore | 5 years ago | |
CODE_OF_CONDUCT.md | 5 years ago | |
CONTRIBUTING.md | 6 years ago | |
LICENSE | 6 years ago | |
PATENTS | 6 years ago | |
README.md | 4 years ago | |
eval_lm.py | 5 years ago | |
fairseq.gif | 6 years ago | |
fairseq_logo.png | 5 years ago | |
generate.py | 4 years ago | |
hubconf.py | 5 years ago | |
interactive.py | 5 years ago | |
preprocess.py | 4 years ago | |
score.py | 5 years ago | |
setup.py | 4 years ago | |
train.py | 4 years ago |
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. It provides reference implementations
of various sequence-to-sequence models, including:
Fairseq features:
We also provide pre-trained models for several benchmark
translation and language modeling datasets.
Please follow the instructions here to install PyTorch: https://github.com/pytorch/pytorch#installation.
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
.
After PyTorch is installed, you can install fairseq with pip
:
pip install fairseq
Installing from source
To install fairseq from source and develop locally:
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable .
Improved training speed
Training speed can be further improved by installing NVIDIA's
apex library with the --cuda_ext
option.
fairseq will automatically switch to the faster modules provided by apex.
The full documentation contains instructions
for getting started, training new models and extending fairseq with new model
types and tasks.
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:
fairseq(-py) is BSD-licensed.
The license applies to the pre-trained models as well.
We also provide an additional patent grant.
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},
}
No Description
Python Cuda C++ Cython Markdown other
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