Metadata-Version: 1.2
Name: contextualized-topic-models
Version: 1.0.0
Summary: Contextualized Topic Models
Home-page: https://github.com/MilaNLProc/contextualized_topic_models
Author: Federico Bianchi
Author-email: f.bianchi@unibocconi.it
License: MIT license
Description: ===========================
        Contextualized Topic Models
        ===========================
        
        
        .. image:: https://img.shields.io/pypi/v/contextualized_topic_models.svg
                :target: https://pypi.python.org/pypi/contextualized_topic_models
        
        .. image:: https://travis-ci.com/MilaNLProc/contextualized-topic-models.svg
                :target: https://travis-ci.com/MilaNLProc/contextualized-topic-models
        
        .. image:: https://readthedocs.org/projects/contextualized-topic-models/badge/?version=latest
                :target: https://contextualized-topic-models.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        Contextualized Topic Models
        
        
        * Free software: MIT license
        * Documentation: https://contextualized-topic-models.readthedocs.io.
        
        Super big shout-out to `Stephen Carrow`_ for creating the awesome https://github.com/estebandito22/PyTorchAVITM package
        from which we constructed the foundations of this package. We are happy to redistribute again this software under the MIT License.
        
        
        Features
        --------
        
        * TODO
        
        
        Quick Guide
        -----------
        
        Install the package using pip
        
        .. code-block:: bash
        
            pip install -U contextualized_topic_models
        
        
        The contextual neural topic model can be easily instantiated using few parameters (although there is a wide range of parameters you can use to change the behaviour of the neural topic model.
        
        .. code-block:: python
        
            cotm = COTM(input_size=1000, bert_input_size=512, inferencetype="contextual")
            cotm.fit()
        
        
        See the example notebook in the `contextualized_topic_models/examples` folder
        
        Team
        ----
        
        * Federico Bianchi <f.bianchi@unibocconi.it> Bocconi University
        * Silvia Terragni <s.terragni4@campus.unimib.it> University of Milan-Bicocca
        * Dirk Hovy <dirk.hovy@unibocconi.it> Bocconi University
        
        Credits
        -------
        
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        To ease the use of the library we have also incuded the `rbo`_ package, all the rights reserved to the author of that package.
        
        
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        .. _`Stephen Carrow` : https://github.com/estebandito22
        .. _`rbo` : https://github.com/dlukes/rbo
        
        
        =======
        History
        =======
        
        0.1.0 (2020-04-04)
        ------------------
        
        * First release on PyPI.
        
Keywords: contextualized_topic_models
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
