Metadata-Version: 1.0
Name: gluonnlp
Version: 0.6.0
Summary: MXNet Gluon NLP Toolkit
Home-page: https://github.com/dmlc/gluon-nlp
Author: Gluon NLP Toolkit Contributors
Author-email: mxnet-gluon@amazon.com
License: Apache-2.0
Description: .. raw:: html
        
           <a href="http://gluon-nlp.mxnet.io/master/index.html"><p align="center"><img width="25%" src="https://github.com/dmlc/gluon-nlp/raw/be3bc8852155e935d68d397e0743715c54c3ce76/docs/_static/gluon_s2.png" /></a>
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           <h3 align="center">
        
        GluonNLP: Your Choice of Deep Learning for NLP
        
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           <a href='http://ci.mxnet.io/job/gluon-nlp/job/master/'><img src='https://img.shields.io/badge/python-2.7%2C%203.6-blue.svg'></a>
           <a href='https://codecov.io/gh/dmlc/gluon-nlp'><img src='https://codecov.io/gh/dmlc/gluon-nlp/branch/master/graph/badge.svg'></a>
           <a href='http://ci.mxnet.io/job/gluon-nlp/job/master/'><img src='http://ci.mxnet.io/job/gluon-nlp/job/master/badge/icon'></a>
           <a href='https://pypi.org/project/gluonnlp/#history'><img src='https://img.shields.io/pypi/v/gluonnlp.svg'></a>
        
        GluonNLP is a toolkit that enables easy text preprocessing, datasets
        loading and neural models building to help you speed up your Natural
        Language Processing (NLP) research.
        
        - `Quick Start Guide <https://github.com/dmlc/gluon-nlp#quick-start-guide>`__
        - `Resources <https://github.com/dmlc/gluon-nlp#resources>`__
        
        News
        ====
        
        - GluonNLP is featured in:
        
          - **AWS re:invent 2018 in Las Vegas, 2018-11-28**! Checkout `details <https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=88736>`_.
          - **KDD 2018 London, 2018-08-21, Apache MXNet Gluon tutorial**! Check out **https://kdd18.mxnet.io**.
        
        Installation
        ============
        
        Make sure you have Python 2.7 or Python 3.6 and recent version of MXNet.
        You can install ``MXNet`` and ``GluonNLP`` using pip.
        
        ``GluonNLP`` is based on the most recent version of ``MXNet``.
        
        
        In particular, if you want to install the most recent ``MXNet`` release:
        
        ::
        
            pip install --upgrade mxnet>=1.3.0
        
        Else, if you want to install the most recent ``MXNet`` nightly build:
        
        ::
        
            pip install --pre --upgrade mxnet
        
        Then, you can install ``GluonNLP``:
        
        ::
        
            pip install gluonnlp
        
        Please check more `installation details <https://github.com/dmlc/gluon-nlp/blob/master/docs/install.rst>`_.
        
        Docs 📖
        =======
        
        GluonNLP documentation is available at `our
        website <http://gluon-nlp.mxnet.io/master/index.html>`__.
        
        Community
        =========
        
        GluonNLP is a community that believes in sharing.
        
        For questions, comments, and bug reports, `Github issues <https://github.com/dmlc/gluon-nlp/issues>`__ is the best way to reach us.
        
        We now have a new Slack channel `here <https://apache-mxnet.slack.com/messages/CCCDM10V9>`__.
        (`register <https://join.slack.com/t/apache-mxnet/shared_invite/enQtNDQyMjAxMjQzMTI3LTkzMzY3ZmRlNzNjNGQxODg0N2Y5NmExMjEwOTZlYmIwYTU2ZTY4ZjNlMmEzOWY5MGQ5N2QxYjhlZTFhZTVmYTc>`__).
        
        How to Contribute
        =================
        
        GluonNLP community welcomes contributions from anyone!
        
        There are lots of opportunities for you to become our `contributors <https://github.com/dmlc/gluon-nlp/blob/master/contributor.rst>`__:
        
        - Ask or answer questions on `GitHub issues <https://github.com/dmlc/gluon-nlp/issues>`__.
        - Propose ideas, or review proposed design ideas on `GitHub issues <https://github.com/dmlc/gluon-nlp/issues>`__.
        - Improve the `documentation <http://gluon-nlp.mxnet.io/master/index.html>`__.
        - Contribute bug reports `GitHub issues <https://github.com/dmlc/gluon-nlp/issues>`__.
        - Write new `scripts <https://github.com/dmlc/gluon-nlp/tree/master/scripts>`__ to reproduce
          state-of-the-art results.
        - Write new `examples <https://github.com/dmlc/gluon-nlp/tree/master/docs/examples>`__ to explain
          key ideas in NLP methods and models.
        - Write new `public datasets <https://github.com/dmlc/gluon-nlp/tree/master/gluonnlp/data>`__
          (license permitting).
        - Most importantly, if you have an idea of how to contribute, then do it!
        
        For a list of open starter tasks, check `good first issues <https://github.com/dmlc/gluon-nlp/labels/good%20first%20issue>`__.
        
        Also see our `contributing
        guide <http://gluon-nlp.mxnet.io/master/how_to/contribute.html>`__ on simple how-tos,
        contribution guidelines and more.
        
        Resources
        =========
        
        Check out how to use GluonNLP for your own research or projects.
        
        If you are new to Gluon, please check out our `60-minute crash course
        <http://gluon-crash-course.mxnet.io/>`__.
        
        For getting started quickly, refer to notebook runnable examples at
        `Examples. <http://gluon-nlp.mxnet.io/master/examples/index.html>`__
        
        For advanced examples, check out our
        `Scripts. <http://gluon-nlp.mxnet.io/master/scripts/index.html>`__
        
        For experienced users, check out our
        `API Notes <http://gluon-nlp.mxnet.io/master/api/index.html>`__.
        
        Quick Start Guide
        =================
        
        `Dataset Loading <http://gluon-nlp.mxnet.io/master/api/notes/data_api.html>`__
        -------------------------------------------------------------------------------
        
        Load the Wikitext-2 dataset, for example:
        
        .. code:: python
        
            >>> import gluonnlp as nlp
            >>> train = nlp.data.WikiText2(segment='train')
            >>> train[0][0:5]
            ['=', 'Valkyria', 'Chronicles', 'III', '=']
        
        `Vocabulary Construction <http://gluon-nlp.mxnet.io/master/api/modules/vocab.html>`__
        -------------------------------------------------------------------------------------
        
        Build vocabulary based on the above dataset, for example:
        
        .. code:: python
        
            >>> vocab = nlp.Vocab(counter=nlp.data.Counter(train[0]))
            >>> vocab
            Vocab(size=33280, unk="<unk>", reserved="['<pad>', '<bos>', '<eos>']")
        
        `Neural Models Building <http://gluon-nlp.mxnet.io/master/api/modules/model.html>`__
        ------------------------------------------------------------------------------------
        
        From the models package, apply a Standard RNN language model to the
        above dataset:
        
        .. code:: python
        
            >>> model = nlp.model.language_model.StandardRNN('lstm', len(vocab),
            ...                                              200, 200, 2, 0.5, True)
            >>> model
            StandardRNN(
              (embedding): HybridSequential(
                (0): Embedding(33280 -> 200, float32)
                (1): Dropout(p = 0.5, axes=())
              )
              (encoder): LSTM(200 -> 200.0, TNC, num_layers=2, dropout=0.5)
              (decoder): HybridSequential(
                (0): Dense(200 -> 33280, linear)
              )
            )
        
        `Word Embeddings Loading <http://gluon-nlp.mxnet.io/master/api/modules/embedding.html>`__
        -----------------------------------------------------------------------------------------
        
        For example, load a GloVe word embedding, one of the state-of-the-art
        English word embeddings:
        
        .. code:: python
        
            >>> glove = nlp.embedding.create('glove', source='glove.6B.50d')
            # Obtain vectors for 'baby' in the GloVe word embedding
            >>> type(glove['baby'])
            <class 'mxnet.ndarray.ndarray.NDArray'>
            >>> glove['baby'].shape
            (50,)
        
        
        New to Deep Learning or NLP?
        ============================
        
        For background knowledge of deep learning or NLP, please refer to the open source book `Dive into Deep Learning <http://en.diveintodeeplearning.org/>`__.
        
Platform: UNKNOWN
