MaartenGr / MaartenGr/BERTopic
deployment of the BERTopic model and monitoring
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- Dominant language
- Python
- Stars
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Description
Hi, first massive congrats and a huge thank you for the library.
I managed to train bertopic on my specific data, and I'm very happy with the relevant results I got.
so I would like to know how to deploy my model reliably, and the most important thing is how to technically do the continuous monitoring (data drift and model drift/deviation), knowing that as performance metrics I use: Coherence score ( c_v , u_mass, npmi ), calinski harabasz score and davies bouldin score.
Thanks in advance.
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
The issue names no files, tests, or entry points. Start by clarifying the intended deployment environment and how data drift, model drift, and the listed coherence and clustering metrics should be monitored. Done would be documented, reliable deployment guidance and a continuous monitoring approach for BERTopic models.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- devops, machine-learning, observability-sre
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100