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topicmodels.etm

Topic Modelling in Embedding Spaces

v0.1.1 · Nov 27, 2025 · MIT + file LICENSE

Description

Find topics in texts which are semantically embedded using techniques like word2vec or Glove. This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at <doi:10.48550/arXiv.1907.04907>.

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies Matrix torch topicmodels.etm

Version History

new 0.1.1 Mar 10, 2026
updated 0.1.1 ← 0.1.0 diff Nov 26, 2025
new 0.1.0 Nov 7, 2021