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        "description": ".. container::\n\n--------------\n\n|Build Status (Travis CI)| |Build Status (AppVeyor)| |Build Status\n(Docs)| |PyPI version|\n\nThe Dynamic Neural Network Toolkit\n\n\n\n\nGeneral\n-------\n\nDyNet is a neural network library developed by Carnegie Mellon\nUniversity and many others. It is written in C++ (with bindings in\nPython) and is designed to be efficient when run on either CPU or GPU,\nand to work well with networks that have dynamic structures that change\nfor every training instance. For example, these kinds of networks are\nparticularly important in natural language processing tasks, and DyNet\nhas been used to build state-of-the-art systems for `syntactic\nparsing <https://github.com/clab/lstm-parser>`__, `machine\ntranslation <https://github.com/neubig/lamtram>`__, `morphological\ninflection <https://github.com/mfaruqui/morph-trans>`__, and many other\napplication areas.\n\nRead the `documentation <http://dynet.readthedocs.io/en/latest/>`__ to\nget started, and feel free to contact the `dynet-users\ngroup <https://groups.google.com/forum/#!forum/dynet-users>`__ group\nwith any questions (if you want to receive email make sure to select\n\u201call email\u201d when you sign up). We greatly appreciate any bug reports and\ncontributions, which can be made by filing an issue or making a pull\nrequest through the `github page <http://github.com/clab/dynet>`__.\n\nYou can also read more technical details in our `technical\nreport <https://arxiv.org/abs/1701.03980>`__.\n\nGetting started\n---------------\n\nYou can find tutorials about using DyNet `here\n(C++) <http://dynet.readthedocs.io/en/latest/tutorial.html#c-tutorial>`__\nand `here\n(python) <http://dynet.readthedocs.io/en/latest/tutorial.html#python-tutorial>`__,\nand `here (EMNLP 2016\ntutorial) <https://github.com/clab/dynet_tutorial_examples>`__.\n\nOne aspect that sets DyNet apart from other tookits is the\n**auto-batching** feature. See the\n`documentation <http://dynet.readthedocs.io/en/latest/minibatch.html>`__\nabout batching.\n\nThe ``example`` folder contains a variety of examples in C++ and python.\n\nInstallation\n------------\n\nDyNet relies on a number of external programs/libraries including CMake,\nEigen, and Mercurial (to install Eigen). CMake, and Mercurial can be\ninstalled from standard repositories.\n\nFor example on **Ubuntu Linux**:\n\n::\n\n   sudo apt-get install build-essential cmake mercurial\n\nOr on **macOS**, first make sure the Apple Command Line Tools are\ninstalled, then get CMake, and Mercurial with either homebrew or\nmacports:\n\n::\n\n   xcode-select --install\n   brew install cmake hg  # Using homebrew.\n   sudo port install cmake mercurial # Using macports.\n\nOn **Windows**, see\n`documentation <http://dynet.readthedocs.io/en/latest/install.html#windows-support>`__.\n\nTo compile DyNet you also need the `development version of the Eigen\nlibrary <https://bitbucket.org/eigen/eigen>`__. **If you use any of the\nreleased versions, you may get assertion failures or compile errors.**\nIf you don\u2019t have Eigen already, you can get it easily using the\nfollowing command:\n\n::\n\n   hg clone https://bitbucket.org/eigen/eigen/ -r b2e267d\n\nThe ``-r NUM`` specified a revision number that is known to work.\nAdventurous users can remove it and use the very latest version, at the\nrisk of the code breaking / not compiling. On macOS, you can install the\nlatest development of Eigen using Homebrew:\n\n::\n\n   brew install --HEAD eigen\n\nC++ installation\n~~~~~~~~~~~~~~~~\n\nYou can install dynet for C++ with the following commands\n\n::\n\n   # Clone the github repository\n   git clone https://github.com/clab/dynet.git\n   cd dynet\n   mkdir build\n   cd build\n   # Run CMake\n   # -DENABLE_BOOST=ON in combination with -DENABLE_CPP_EXAMPLES=ON also\n   # compiles the multiprocessing c++ examples\n   cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen -DENABLE_CPP_EXAMPLES=ON\n   # Compile using 2 processes\n   make -j 2\n   # Test with an example\n   ./examples/train_xor\n\nFor more details refer to the\n`documentation <http://dynet.readthedocs.io/en/latest/install.html#building>`__\n\nPython installation\n~~~~~~~~~~~~~~~~~~~\n\nYou can install DyNet for python by using the following command\n\n::\n\n   pip install git+https://github.com/clab/dynet#egg=dynet\n\nFor more details refer to the\n`documentation <http://dynet.readthedocs.io/en/latest/python.html#installing-dynet-for-python>`__\n\nCiting\n------\n\nIf you use DyNet for research, please cite this report as follows:\n\n::\n\n   @article{dynet,\n     title={DyNet: The Dynamic Neural Network Toolkit},\n     author={Graham Neubig and Chris Dyer and Yoav Goldberg and Austin Matthews and Waleed Ammar and Antonios Anastasopoulos and Miguel Ballesteros and David Chiang and Daniel Clothiaux and Trevor Cohn and Kevin Duh and Manaal Faruqui and Cynthia Gan and Dan Garrette and Yangfeng Ji and Lingpeng Kong and Adhiguna Kuncoro and Gaurav Kumar and Chaitanya Malaviya and Paul Michel and Yusuke Oda and Matthew Richardson and Naomi Saphra and Swabha Swayamdipta and Pengcheng Yin},\n     journal={arXiv preprint arXiv:1701.03980},\n     year={2017}\n   }\n\nContributing\n------------\n\nWe welcome any contribution to DyNet! 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