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        "description": "# Sockeye\n\n[![PyPI version](https://badge.fury.io/py/sockeye.svg)](https://badge.fury.io/py/sockeye)\n[![GitHub license](https://img.shields.io/github/license/awslabs/sockeye.svg)](https://github.com/awslabs/sockeye/blob/master/LICENSE)\n[![GitHub issues](https://img.shields.io/github/issues/awslabs/sockeye.svg)](https://github.com/awslabs/sockeye/issues)\n[![Build Status](https://travis-ci.org/awslabs/sockeye.svg?branch=master)](https://travis-ci.org/awslabs/sockeye)\n[![Documentation Status](https://readthedocs.org/projects/sockeye/badge/?version=latest)](http://sockeye.readthedocs.io/en/latest/?badge=latest)\n\nThis package contains the Sockeye project, a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet (Incubating).\nIt implements state-of-the-art encoder-decoder architectures, such as:\n\n- Deep Recurrent Neural Networks with Attention [[Bahdanau, '14](https://arxiv.org/abs/1409.0473)]\n- Transformer Models with self-attention [[Vaswani et al, '17](https://arxiv.org/abs/1706.03762)]\n- Fully convolutional sequence-to-sequence models [[Gehring et al, '17](https://arxiv.org/abs/1705.03122)]\n\nIn addition, it provides an experimental [image-to-description module](https://github.com/awslabs/sockeye/tree/master/sockeye/image_captioning) that can be used for image captioning.\nRecent developments and changes are tracked in our [CHANGELOG](https://github.com/awslabs/sockeye/blob/master/CHANGELOG.md).\n\nIf you have any questions or discover problems, please [file an issue](https://github.com/awslabs/sockeye/issues/new).\nYou can also send questions to *sockeye-dev-at-amazon-dot-com*.\n\n## Documentation\n\nFor information on how to use Sockeye, please visit [our documentation](https://awslabs.github.io/sockeye/).\nDevelopers may be interested in our [developer guidelines](https://awslabs.github.io/sockeye/development.html).\n\n## Citation\n\nFor technical information about Sockeye, see our paper on the arXiv ([BibTeX](sockeye.bib)):\n\n> Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post. 2017.\n> [Sockeye: A Toolkit for Neural Machine Translation](https://arxiv.org/abs/1712.05690). ArXiv e-prints.\n\n\n\n\n",
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