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A scikit-learn extension for Topic Modelling with Hierarchical Stochastic Block Models

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topsbm - A scikit-learn extension for Topic Models based on Stochastic Block Models
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Martin Gerlach, Tiago P. Peixoto, and Eduardo G. Altmann, `“A network approach to topic models,” <http://advances.sciencemag.org/content/4/7/eaaq1360>`_ Science Advances (2018)

Software ported to Scikit-learn format by the `Sydney Informatics Hub <https://informatics.sydney.edu.au>`__ at the University of Sydney.

.. |py-versions| image:: https://img.shields.io/pypi/pyversions/topsbm.svg
:alt: Python versions supported

.. |version| image:: https://badge.fury.io/py/topsbm.svg
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.. |build| image:: https://travis-ci.org/Sydney-Informatics-Hub/topsbm.svg?branch=master
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:target: https://travis-ci.org/Sydney-Informatics-Hub/topsbm

.. |issues| image:: https://img.shields.io/github/issues/Sydney-Informatics-Hub/topsbm.svg
:alt: Issue tracker
:target: https://github.com/Sydney-Informatics-Hub/topsbm

.. |coverage| image:: https://coveralls.io/repos/github/Sydney-Informatics-Hub/topsbm/badge.svg
:alt: Test coverage
:target: https://coveralls.io/github/Sydney-Informatics-Hub/topsbm

.. |docs| image:: https://readthedocs.org/projects/topsbm/badge/?version=latest
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.. |licence| image:: https://img.shields.io/badge/Licence-GPLv3-blue.svg
:target: https://opensource.org/licenses/gpl-license

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