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I've built a topic model, with:

  • Input: list of tokenized lists
  • Output: a m x t matrix (with each cell indicating the probability of word i appearing in topic k).
  • Output: a k x n matrix (with each cell indicating the probability of topic k in document j).

To find the optimal number of topics, I want to calculate the coherence for a model. However, I am only aware of sklearn's Coherencemodel, which seems to require a Gensim model as input.

Are there any other packages/implementations that I could use to calculate the coherence of a computed topic model? Or, if it is indeed possible to use the Coherencemodel without inputting a LDAmodel, could someone show me how to do that?

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