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To find more about the underlying algorithm and assumptions here.

Represent topics with one or multiple representations.Clustering reduced embeddings into topics.The following steps are completely modular: You can swap out any of these models or even remove them entirely. In other words, BERTopic not only allows you to build your own topic model but to explore several topic modeling techniques on top of your customized topic model: However, it assumes some independence between these steps which makes BERTopic quite modular.

Modularity ¶īy default, the main steps for topic modeling with BERTopic are sentence-transformers, UMAP, HDBSCAN, and c-TF-IDF run in sequence. Instead of iterating over all of these different topic representations, you can model them simultaneously with multi-aspect topic representations in BERTopic.