Model load time about bertopic HOT 1 CLOSED

The most time-consuming part of both the transform() and fit_transform() methods is the extraction of BERT-based embeddings. When you have trained the model, you will still need to extract the embeddings for unseen documents. Unfortunately, this means that it is difficult to speed up transform() as it, computationally, mostly relies on extracting the embedding.

Fortunately, in the case of topic modeling, it is unlikely that you will frequently re-train the model as that would result in creating new topics that need to be interpreted again. Often, you will fit_transform() on a large dataset and use transform() for unseen documents, which is faster as the number of unseen documents are likely to be less frequent.

Having said that, it would be nice to be able to swap out BERT-embeddings for perhaps another feature extraction method. This could result in having a much faster application although it could hinder the quality of the generated clusters. Perhaps flair might be an interesting alternative.

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