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78 lines
3.9 KiB
Markdown
78 lines
3.9 KiB
Markdown
Marian
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======
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[](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-9.2/)
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[](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cuda-10.1/)
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[](http://vali.inf.ed.ac.uk/jenkins/job/marian-dev-cpu/)
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[](http://vali.inf.ed.ac.uk/jenkins/job/marian-regression-tests/)
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[](https://github.com/marian-nmt/marian/releases)
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[](./LICENSE.md)
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[](https://twitter.com/intent/follow?screen_name=marian_nmt)
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*Marian* is an efficient Neural Machine Translation framework written in pure
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C++ with minimal dependencies.
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Named in honour of Marian Rejewski, a Polish mathematician and cryptologist.
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Main features:
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- Efficient pure C++ implementation
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- Fast multi-GPU training and GPU/CPU translation
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- State-of-the-art NMT architectures: deep RNN and transformer
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- Permissive open source license (MIT)
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- [more detail...](https://marian-nmt.github.io/features)
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If you use this, please cite:
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Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth
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Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay
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Bogoychev, André F. T. Martins, Alexandra Birch (2018). Marian: Fast Neural
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Machine Translation in C++ (http://www.aclweb.org/anthology/P18-4020)
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@InProceedings{mariannmt,
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title = {Marian: Fast Neural Machine Translation in {C++}},
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author = {Junczys-Dowmunt, Marcin and Grundkiewicz, Roman and
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Dwojak, Tomasz and Hoang, Hieu and Heafield, Kenneth and
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Neckermann, Tom and Seide, Frank and Germann, Ulrich and
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Fikri Aji, Alham and Bogoychev, Nikolay and
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Martins, Andr\'{e} F. T. and Birch, Alexandra},
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booktitle = {Proceedings of ACL 2018, System Demonstrations},
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pages = {116--121},
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publisher = {Association for Computational Linguistics},
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year = {2018},
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month = {July},
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address = {Melbourne, Australia},
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url = {http://www.aclweb.org/anthology/P18-4020}
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}
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## Amun
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The handwritten decoder for RNN models compatible with Marian and Nematus has
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been superseded by the Marian decoder. The code is available in a separate
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repository: https://github.com/marian-nmt/amun
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## Website
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More information on https://marian-nmt.github.io
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- [Quick start](https://marian-nmt.github.io/quickstart)
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- [Installation and usage documentation](https://marian-nmt.github.io/docs)
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- [Usage examples](https://marian-nmt.github.io/examples)
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## Acknowledgements
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The development of Marian received funding from the European Union's
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_Horizon 2020 Research and Innovation Programme_ under grant agreements
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688139 ([SUMMA](http://www.summa-project.eu); 2016-2019),
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645487 ([Modern MT](http://www.modernmt.eu); 2015-2017),
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644333 ([TraMOOC](http://tramooc.eu/); 2015-2017),
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644402 ([HiML](http://www.himl.eu/); 2015-2017),
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825303 ([Bergamot](https://browser.mt/); 2019-2021),
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the Amazon Academic Research Awards program,
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the World Intellectual Property Organization,
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and is based upon work supported in part by the Office of the Director of
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National Intelligence (ODNI), Intelligence Advanced Research Projects Activity
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(IARPA), via contract #FA8650-17-C-9117.
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This software contains source code provided by NVIDIA Corporation.
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