MaartenGr / MaartenGr/BERTopic
Does BERTopic rely on *both* sentence_embeddings and word_embeddings
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- Dominant language
- Python
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Description
When exploring relationships *between* topics (2D visualisations, hierarchy) we need to represent *each topic* as a summary vector (cluster-level embedding).
The BERTopic source code stats
> `topic_embeddings_ (np.ndarray) : The embeddings for each topic. It is calculated by taking the weighted average of word embeddings in a topic based on their c-TF-IDF values.`
This seems to imply BERTopic needs *both* a sentence-level word embedding model and a word-level embedding model.
Is this the case? Where is this specified in the source code please?
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Research direction
Start by tracing the source-code references to `topic_embeddings_`, then inspect the code paths for topic visualisations and hierarchy generation. Determine whether those paths require both sentence-level and word-level embeddings, and document the supported dependency or embedding requirements once confirmed.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100