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        "description": "GluonNLP: Your Choice of Deep Learning for NLP\n\nGluonNLP is a toolkit that enables easy text preprocessing, datasets\nloading and neural models building to help you speed up your Natural\nLanguage Processing (NLP) research.\n\n- `Quick Start Guide <https://github.com/dmlc/gluon-nlp#quick-start-guide>`__\n- `Resources <https://github.com/dmlc/gluon-nlp#resources>`__\n\nNews\n====\n\n- GluonNLP will be featured in KDD 2019 Alaska! Check out our tutorial: `From Shallow to Deep Language Representations: Pre-training, Fine-tuning, and Beyond <https://www.kdd.org/kdd2019/hands-on-tutorials>`__.\n- Tutorial proposal for GluonNLP is accepted at `EMNLP 2019 <https://www.emnlp-ijcnlp2019.org>`__, Hong Kong.\n\n- GluonNLP was featured in:\n\n  - **JSALT 2019 in Montreal, 2019-6-14**! Checkout **https://jsalt19.mxnet.io**.\n  - **AWS re:invent 2018 in Las Vegas, 2018-11-28**! Checkout `details <https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=88736>`_.\n  - **PyData 2018 NYC, 2018-10-18**! Checkout the `awesome talk <https://pydata.org/nyc2018/schedule/presentation/76/>`__ by Sneha Jha.\n  - **KDD 2018 London, 2018-08-21, Apache MXNet Gluon tutorial**! Check out **https://kdd18.mxnet.io**.\n\nInstallation\n============\n\nMake sure you have Python 3.6 or newer and a recent version of MXNet (our CI\nserver runs the testsuite with Python 3.6).\n\nYou can install ``MXNet`` and ``GluonNLP`` using pip.\n\n``GluonNLP`` is based on the most recent version of ``MXNet``.\n\n\nIn particular, if you want to install the most recent ``MXNet`` release:\n\n::\n\n    pip install --upgrade mxnet>=1.5.0\n\nElse, if you want to install the most recent ``MXNet`` nightly build:\n\n::\n\n    pip install --pre --upgrade mxnet\n\nThen, you can install ``GluonNLP``:\n\n::\n\n    pip install gluonnlp\n\nPlease check more `installation details <https://github.com/dmlc/gluon-nlp/blob/master/docs/install.rst>`_.\n\nDocs \ud83d\udcd6\n=======\n\nGluonNLP documentation is available at `our\nwebsite <http://gluon-nlp.mxnet.io/master/index.html>`__.\n\nCommunity\n=========\n\nGluonNLP is a community that believes in sharing.\n\nFor questions, comments, and bug reports, `Github issues <https://github.com/dmlc/gluon-nlp/issues>`__ is the best way to reach us.\n\nWe now have a new Slack channel `here <https://apache-mxnet.slack.com/messages/CCCDM10V9>`__.\n(`register <https://join.slack.com/t/apache-mxnet/shared_invite/enQtNDQyMjAxMjQzMTI3LTkzMzY3ZmRlNzNjNGQxODg0N2Y5NmExMjEwOTZlYmIwYTU2ZTY4ZjNlMmEzOWY5MGQ5N2QxYjhlZTFhZTVmYTc>`__).\n\nHow to Contribute\n=================\n\nGluonNLP community welcomes contributions from anyone!\n\nThere are lots of opportunities for you to become our `contributors <https://github.com/dmlc/gluon-nlp/blob/master/contributor.rst>`__:\n\n- Ask or answer questions on `GitHub issues <https://github.com/dmlc/gluon-nlp/issues>`__.\n- Propose ideas, or review proposed design ideas on `GitHub issues <https://github.com/dmlc/gluon-nlp/issues>`__.\n- Improve the `documentation <http://gluon-nlp.mxnet.io/master/index.html>`__.\n- Contribute bug reports `GitHub issues <https://github.com/dmlc/gluon-nlp/issues>`__.\n- Write new `scripts <https://github.com/dmlc/gluon-nlp/tree/master/scripts>`__ to reproduce\n  state-of-the-art results.\n- Write new `examples <https://github.com/dmlc/gluon-nlp/tree/master/docs/examples>`__ to explain\n  key ideas in NLP methods and models.\n- Write new `public datasets <https://github.com/dmlc/gluon-nlp/tree/master/gluonnlp/data>`__\n  (license permitting).\n- Most importantly, if you have an idea of how to contribute, then do it!\n\nFor a list of open starter tasks, check `good first issues <https://github.com/dmlc/gluon-nlp/labels/good%20first%20issue>`__.\n\nAlso see our `contributing\nguide <http://gluon-nlp.mxnet.io/master/how_to/contribute.html>`__ on simple how-tos,\ncontribution guidelines and more.\n\nResources\n=========\n\nCheck out how to use GluonNLP for your own research or projects.\n\nIf you are new to Gluon, please check out our `60-minute crash course\n<http://gluon-crash-course.mxnet.io/>`__.\n\nFor getting started quickly, refer to notebook runnable examples at\n`Examples. <http://gluon-nlp.mxnet.io/master/examples/index.html>`__\n\nFor advanced examples, check out our\n`Scripts. <http://gluon-nlp.mxnet.io/master/scripts/index.html>`__\n\nFor experienced users, check out our\n`API Notes <http://gluon-nlp.mxnet.io/master/api/index.html>`__.\n\nQuick Start Guide\n=================\n\n`Dataset Loading <http://gluon-nlp.mxnet.io/master/api/notes/data_api.html>`__\n-------------------------------------------------------------------------------\n\nLoad the Wikitext-2 dataset, for example:\n\n.. code:: python\n\n    >>> import gluonnlp as nlp\n    >>> train = nlp.data.WikiText2(segment='train')\n    >>> train[0:5]\n    ['=', 'Valkyria', 'Chronicles', 'III', '=']\n\n`Vocabulary Construction <http://gluon-nlp.mxnet.io/master/api/modules/vocab.html>`__\n-------------------------------------------------------------------------------------\n\nBuild vocabulary based on the above dataset, for example:\n\n.. code:: python\n\n    >>> vocab = nlp.Vocab(counter=nlp.data.Counter(train))\n    >>> vocab\n    Vocab(size=33280, unk=\"<unk>\", reserved=\"['<pad>', '<bos>', '<eos>']\")\n\n`Neural Models Building <http://gluon-nlp.mxnet.io/master/api/modules/model.html>`__\n------------------------------------------------------------------------------------\n\nFrom the models package, apply a Standard RNN language model to the\nabove dataset:\n\n.. code:: python\n\n    >>> model = nlp.model.language_model.StandardRNN('lstm', len(vocab),\n    ...                                              200, 200, 2, 0.5, True)\n    >>> model\n    StandardRNN(\n      (embedding): HybridSequential(\n        (0): Embedding(33280 -> 200, float32)\n        (1): Dropout(p = 0.5, axes=())\n      )\n      (encoder): LSTM(200 -> 200.0, TNC, num_layers=2, dropout=0.5)\n      (decoder): HybridSequential(\n        (0): Dense(200 -> 33280, linear)\n      )\n    )\n\n`Word Embeddings Loading 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Zhang, Hang and Zhang, Zhi and Zhang, Zhongyue and Zheng, Shuai},\n     journal={arXiv preprint arXiv:1907.04433},\n     year={2019}\n   }\n\n\nNew to Deep Learning or NLP?\n============================\n\nFor background knowledge of deep learning or NLP, please refer to the open source book `Dive into Deep Learning <http://en.diveintodeeplearning.org/>`__.",
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