Metadata-Version: 2.0
Name: vsmlib
Version: 0.1.20
Summary: toolbox for various tasks in the area of vector space models of computational linguistic
Home-page: http://vsm.blackbird.pw/
Author: UNKNOWN
Author-email: UNKNOWN
License: UNKNOWN
Description-Content-Type: UNKNOWN
Keywords: NLP,linguistics,language
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Text Processing :: Linguistic
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: sklearn
Requires-Dist: matplotlib
Requires-Dist: brewer2mpl
Requires-Dist: tables
Requires-Dist: tqdm
Requires-Dist: progressbar2
Requires-Dist: pyyaml
Requires-Dist: chainer
Requires-Dist: system-query
Requires-Dist: pillow

VSMlib
******

.. image:: https://api.travis-ci.org/undertherain/vsmlib.svg?branch=master
    :target: https://travis-ci.org/undertherain/vsmlib
    :alt: build status from Travis CI

.. image:: https://coveralls.io/repos/github/undertherain/vsmlib/badge.svg?branch=master
    :target: https://coveralls.io/github/undertherain/vsmlib?branch=master
    :alt: coveralls badge

.. image:: https://badge.fury.io/py/vsmlib.svg
    :target: https://badge.fury.io/py/vsmlib
    :alt: pypi version

VSMlibs helps to perform a range of tasks within a framework of vector space models of computational linguistics.

What functionality is included
==============================

* creating word embeddings by counting and neural-based methods, including sub-word-level models
* importing and exporting from a banch of popular formats of word embeddings and providing unified access to word-vectors
* perfroming a range of downstream tasks / benchmarks on embeddings
* visualising embeddings

How do I get set up?
====================

* ``pip3 install vsmlib`` for stable version
* ``pip3 install git+https://github.com/undertherain/vsmlib.git`` for latest dev version
* Python 3.5 or later is required


📖 Documentation
================

=================== ===
`Tutorial`_         vsmlib overview and end-to-end examples.
`API Reference`_    The detailed reference for vsmlib API.
`Contribute`_       How to contribute to the vsmlib project and code base.
=================== ===

.. _Tutorial: http://vsmlib.readthedocs.io/en/latest/tutorial/index.html
.. _API Reference: http://vsmlib.readthedocs.io/en/latest/reference/index.html
.. _Contribute: http://vsmlib.readthedocs.io/en/latest/tutorial/contribution.html


Who do I talk to?
=================

* Issue tracker is the way to go

