MXNet Gluon NLP Toolkit
Project description
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<h3 align="center">
GluonNLP: Your Choice of Deep Learning for NLP
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</h3>
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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 ๐
======
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>``\ \_\_
โโโโโโโโโโโโโโโโโโโโโโโโโโโโ-
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>``\ \_\_
โโโโโโโโโโโโโโโโโโโโโโโโโโโ
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>``\ \_\_
โโโโโโโโโโโโโโโโโโโโโโโโโโโโ
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>``\ \_\_
โโโโโโโโโโโโโโโโโโโโโโโโโโโ
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,)
.. raw:: html
<p align="center">
.. raw:: html
</p>
.. raw:: html
.. raw:: html
<h3 align="center">
GluonNLP: Your Choice of Deep Learning for NLP
.. raw:: html
.. raw:: html
</h3>
.. raw:: html
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 ๐
======
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>``\ \_\_
โโโโโโโโโโโโโโโโโโโโโโโโโโโโ-
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>``\ \_\_
โโโโโโโโโโโโโโโโโโโโโโโโโโโ
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>``\ \_\_
โโโโโโโโโโโโโโโโโโโโโโโโโโโโ
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>``\ \_\_
โโโโโโโโโโโโโโโโโโโโโโโโโโโ
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,)
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