Metadata-Version: 1.0
Name: gluonnlp
Version: 0.2.0
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
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        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='http://ci.mxnet.io/job/gluon-nlp/job/master/'><img src='https://codecov.io/gh/leezu/gluon-nlp/branch/master/graph/badge.svg?token=xQ2HKDk9ux'></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>
        
        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 <#quick-start-guide>`__
        - `Resources <#resources>`__
        
        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:
        
        ::
        
            pip install --pre --upgrade mxnet
            pip install gluonnlp
        
        Docs 📖
        ======
        
        GluonNLP documentation is available at `our
        website <http://gluon-nlp.mxnet.io/master/index.html>`__.
        
        Community
        =========
        
        For questions and comments, please visit our `forum <https://discuss.mxnet.io/>`__
        (and `Chinese version <https://discuss.gluon.ai/>`__). For bug reports, please submit Github issues.
        
        How to Contribute
        =================
        
        GluonNLP has been developed by community members. Everyone is
        more than welcome to contribute. We together can make the GluonNLP better
        and more user-friendly to more users.
        
        Read our `contributing
        guide <http://gluon-nlp.mxnet.io/master/how_to/contribute.html>`__ to get
        to know about our development procedure, how to propose bug fixes and
        improvements, as well as how to build and test your changes to GluonNLP.
        
        Join our `contributors <contributor.rst>`__.
        
        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/data.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/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/model.html>`__
        -----------------------------------------------------------------------------------
        
        From the models package, apply an Standard RNN langauge 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/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,)
        
Platform: UNKNOWN
