Metadata-Version: 2.1
Name: prosit
Version: 0.1.5
Summary: A topic models algorithm
Home-page: https://github.com/fornaciari/prosit
Author: Tommaso Fornaciari
Author-email: tommaso.fornaciari@poliziadistato.it
License: MIT license
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
License-File: LICENSE
License-File: AUTHORS.rst

prosit - ProSiT!
================

.. image:: https://img.shields.io/pypi/v/prosit.svg
        :target: https://pypi.python.org/pypi/prosit

.. image:: https://img.shields.io/github/license/fornaciari/prosit
        :target: https://lbesson.mit-license.org/
        :alt: License

.. image:: https://github.com/fornaciari/prosit/workflows/Python%20Package/badge.svg
        :target: https://github.com/fornaciari/prosit/actions

.. image:: https://readthedocs.org/projects/boostsa/badge/?version=latest
        :target: https://prosit.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status

.. image:: https://colab.research.google.com/assets/colab-badge.svg
    :target: https://colab.research.google.com/drive/1eewGMqW_cIRqKdWW1tBCFE3T2qVCI_EV#scrollTo=6czDoYOiGpJx
    :alt: Open In Colab

Welcome
-------

ProSiT - PROgressive SImilarity Thresholds is an algorithm for topic models.

Documentation at `readthedocs <https://prosit.readthedocs.io/en/latest/?badge=latest>`_.

You can try ProSiT on this `colab notebook <https://colab.research.google.com/drive/1eewGMqW_cIRqKdWW1tBCFE3T2qVCI_EV#scrollTo=6czDoYOiGpJx>`_.

For technical details and citation, please refer to:

 `Fornaciari T., Hovy D., Bianchi F. (2022).
 *ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds*.
 arXiv <https://arxiv.org/abs/2210.14763>`_


