Metadata-Version: 2.1
Name: turftopic
Version: 0.1.0
Summary: Topic modeling with contextual representations from sentence transformers.
License: MIT
Author: Márton Kardos
Author-email: power.up1163@gmail.com
Requires-Python: >=3.9,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Provides-Extra: pyro-ppl
Requires-Dist: numpy (>=1.23.0,<2.0.0)
Requires-Dist: pyro-ppl (>=1.8.0,<2.0.0) ; extra == "pyro-ppl"
Requires-Dist: rich (>=13.6.0,<14.0.0)
Requires-Dist: scikit-learn (>=1.2.0,<2.0.0)
Requires-Dist: scipy (>=1.10.0,<2.0.0)
Requires-Dist: sentence-transformers (>=2.2.0,<3.0.0)
Requires-Dist: torch (>=2.1.0,<3.0.0)
Description-Content-Type: text/markdown

<p align="center">
<img align="center" height="200" src="assets/logo_w_text.svg">
<br>
 <b>Your go-to package for topic modeling with contextual representations from transformers. </b></p>

### Intentions:
 - Provide simple, robust and fast implementations of existing approaches (BERTopic, Top2Vec, CTM) with minimal dependencies.
 - Implement state-of-the-art approaches from my papers. (papers work-in-progress)
 - Put all approaches in a broader conceptual framework.
 - Provide clear and extensive documentation about the best use-cases for each model.
 - Make the models' API streamlined and compatible with topicwizard and scikit-learn.
 - Develop smarter, transformer-based evaluation metrics.

(Stay tuned...)

