MaartenGr / MaartenGr/BERTopic
Getting probabilities for all topics given a document from loaded model
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- Dominant language
- Python
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Description
I extracted my model using safetensors. In the original model I used cacluate_probabilities = True. When I loaded the model I wanted to predict the topics and the probabilities for a topic using the transform method.
The returned tuple was just the topic and the probability. Is there a way I can extract the probabilities for all the topics ? My goal is to get the top 10 topics for a document.
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 transform method and the path for loading a model extracted with safetensors. Trace how the tuple containing a topic and probability is produced, then verify that the completed behavior exposes probabilities for all topics so a document's top 10 topics can be selected.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100