MaartenGr / MaartenGr/BERTopic
label topics with llama2 and then reduce outliers?
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- Python
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Description
I followed your YouTube tutorial to label topics with llama2.
I also want to do reduce outliers using the strategy of embeddings, but not sure which step should I take. I did topic.model.fit_tranform() then I did reduce outliers, and the custom labels not right....
thanks!
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the BERTopic tutorial referenced in the issue and the sequence around topic.model.fit_tranform() and reduce outliers. Reproduce the custom-label result with the same embedding-based strategy, then establish what label output is expected and whether the reported mismatch is reproducible.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100