While topic models are complex, they don't have to be complicated! Our two-step install process and flexible workflow asks you what you want to do and guides you to the next step of exploration and interpretation.
InPhO-TE was designed for personal computing. Your data can stay on your own machine - Mac, Windows, or Linux - right under your fingertips.
While designed for personal computing, InPhO-TE can easily deploy to the cloud for greater computing power for training and to serve the resulting models, like the ones on this site.
The permissive MIT License means that you can use InPhO-TE however you like. All source code is available on GitHub and all related projects are unrestricted.
pip install topicexplorer
topicexplorer init /path/to/corpus
topicexplorer prep /path/to/corpus
topicexplorer train /path/to/corpus
topicexplorer launch /path/to/corpus
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There are three InPhO-TE mailing lists:
Murdock, Jaimie and Allen, Colin. "Visualization Techniques for Topic Model Checking" AAAI Conference on Artificial Intelligence 2015. Available at: https://www.hypershelf.org/.