MaartenGr / MaartenGr/BERTopic
Guidance on managing BERTopic models
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- Dominant language
- Python
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Description
I'm seeking some advice on managing BERTopic models for efficient topic clustering.
I've utilised BERTopic to cluster approximately 13,000 data which is three months data, resulting in around 130 distinct topics. To further reduce the number of topics, I implemented hierarchical topic modelling technique, effectively reducing the number of topics to 100.
Upon thorough analysis of both the topics and the associated documents, I've identified the necessity for additional topic merging. As a result, I manually merged several topics to refine the clustering outcome.
Now, I'm at a stage where I need to preserve this final model for future use. Specifically, I aim to employ it for predicting the topics of upcoming month's data.
I would greatly appreciate any insights or suggestions.
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
No files, tests, or entry points are named. Start by determining the supported workflow for preserving manually merged BERTopic models and applying them to future data; done would be clear, documented guidance for that workflow.
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
- 20/100