MaartenGr / MaartenGr/BERTopic
Topic Modelling on longer documents
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- Dominant language
- Python
- Stars
- 7.8k
- Forks
- 920
- Avg merge
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- Merged PRs (30d)
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Description
I'm hoping to use BERTopic for extracting topics from longer documents: >10 documents, each containing ~3-20 pages of text.
Are there any special methods, tips or documentation on how to use BERTopic for such use cases?
Thanks for the excellent package!
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 with BERTopic's existing documentation and examples, focusing on usage for documents of the stated length and count. Done would be clear, documented guidance for handling this use case, including any relevant methods or limitations; the issue names no source file or test to update.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100