cd9afea8d3
* add initial guidelines of code documentation * fix math formula not displayed in Sphinx * remove @name tags which cannot be extracted by exhale and cause function signature errors * fix markdown ref warning and update markdown parser in sphinx * more about doxygen: add Doxygen commands and math formulas * move code doc guide to a new .rst file * add formula image * Set myst-parser version appropriate for the requested sphinx version * Update documentation on how to write Doxygen comments * Add new section to the documentation index * Sphinx 2.4.4 requires myst-parser 0.14 * complete code doc guide and small fixes on reStructuredText formats * More about reStructuredText * Update badges on the documentation frontpage Co-authored-by: Roman Grundkiewicz <rgrundkiewicz@gmail.com> |
||
|---|---|---|
| .github | ||
| cmake | ||
| contrib | ||
| doc | ||
| examples@6d5921cc7d | ||
| regression-tests@32a2f7960d | ||
| scripts | ||
| src | ||
| vs | ||
| .clang-format | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| azure-pipelines.yml | ||
| CHANGELOG.md | ||
| CMakeLists.txt | ||
| CMakeSettings.json | ||
| CONTRIBUTING.md | ||
| Doxyfile.in | ||
| LICENSE.md | ||
| README.md | ||
| VERSION | ||
Marian
Marian is an efficient Neural Machine Translation framework written in pure C++ with minimal dependencies.
Named in honour of Marian Rejewski, a Polish mathematician and cryptologist.
Main features:
- Efficient pure C++ implementation
- Fast multi-GPU training and GPU/CPU translation
- State-of-the-art NMT architectures: deep RNN and transformer
- Permissive open source license (MIT)
- more detail...
If you use this, please cite:
Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, Alexandra Birch (2018). Marian: Fast Neural Machine Translation in C++ (http://www.aclweb.org/anthology/P18-4020)
@InProceedings{mariannmt,
title = {Marian: Fast Neural Machine Translation in {C++}},
author = {Junczys-Dowmunt, Marcin and Grundkiewicz, Roman and
Dwojak, Tomasz and Hoang, Hieu and Heafield, Kenneth and
Neckermann, Tom and Seide, Frank and Germann, Ulrich and
Fikri Aji, Alham and Bogoychev, Nikolay and
Martins, Andr\'{e} F. T. and Birch, Alexandra},
booktitle = {Proceedings of ACL 2018, System Demonstrations},
pages = {116--121},
publisher = {Association for Computational Linguistics},
year = {2018},
month = {July},
address = {Melbourne, Australia},
url = {http://www.aclweb.org/anthology/P18-4020}
}
Amun
The handwritten decoder for RNN models compatible with Marian and Nematus has been superseded by the Marian decoder. The code is available in a separate repository: https://github.com/marian-nmt/amun
Website
More information on https://marian-nmt.github.io
Acknowledgements
The development of Marian received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreements 688139 (SUMMA; 2016-2019), 645487 (Modern MT; 2015-2017), 644333 (TraMOOC; 2015-2017), 644402 (HiML; 2015-2017), 825303 (Bergamot; 2019-2021), the Amazon Academic Research Awards program, the World Intellectual Property Organization, and is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract #FA8650-17-C-9117.
This software contains source code provided by NVIDIA Corporation.