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Myle Ott df2f84ce61 | 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 | 4 years ago | |
CODE_OF_CONDUCT.md | 4 years ago | |
CONTRIBUTING.md | 4 years ago | |
LICENSE | 4 years ago | |
README.md | 4 years ago | |
eval_lm.py | 4 years ago | |
fairseq.gif | 6 years ago | |
fairseq_logo.png | 5 years ago | |
generate.py | 4 years ago | |
hubconf.py | 4 years ago | |
interactive.py | 4 years ago | |
preprocess.py | 4 years ago | |
score.py | 4 years ago | |
setup.py | 4 years ago | |
train.py | 4 years ago | |
validate.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.
Fairseq provides reference implementations of various sequence-to-sequence models, including:
Additionally:
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.
--cuda_ext
optionTo install fairseq:
pip install fairseq
On MacOS:
CFLAGS="-stdlib=libc++" pip install fairseq
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
.
Installing from source
To install fairseq from source and develop locally:
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable .
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 MIT-licensed.
The license applies to the pre-trained models as well.
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},
}
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Python Cuda C++ Cython Markdown other
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