Comments (3)
It is difficult to say without knowing a bit more. Can you share your full code? Also, can you show an example of the hierarchy that was created? Lastly, which version of BERTopic are you using?
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I can't really share the code nor the topics as they are company private. But I thought about just taking the topic representations and feed them back to BERTopic and use fine-tuning , what do you think ? So that the clustering is more robust ? ( I use version 0.16.0 )
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It is really difficult to say without knowing specifically how you created the model, there might be something going on there. Having said that, the hierarchical modeling is done using the c-TF-IDF representations I believe but you could also use the topic embeddings instead which might help in this case.
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Related Issues (20)
- approximate_distribution returns only 0s HOT 5
- Feature (Watsonx): representations using Llama-3-70b and Mixtral-8x7b HOT 1
- Which hyper parameter mostly influence the number of topics for Chinese texts? HOT 3
- Zero-Shot Topic Modelling and Topics Over Time HOT 1
- Loading of saved model returns Error: "This BERTopic instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
- Creating representations using IBM Watsonx LLMs HOT 5
- c_tf_idf_ is None when using zero shot topic modeling. HOT 1
- Issue with Scikit-learn 1.5.0
- Error at Combining clustered topics with the zeroshot model HOT 2
- Compare LDA, NMF, LSA with BERTopic (w/ embedding: all-MiniLM-L6-v2 + dim_red: UMAP + cluster: HDBSCAN) HOT 1
- AttributeError: 'BertModel' object has no attribute 'attn_implementation' #30965 HOT 3
- Zeroshot Topic Modeling With no Embedding Model HOT 1
- Extending ".visulize_document_datamap" with "label_over_points"-flag HOT 1
- Zero shot topic model with pre embedded zero shot topics HOT 1
- Why outliers are shown in visualize_documents after outlier reduction? HOT 2
- Guided topic model with pre embedded `seed_topic_list` HOT 1
- probabilities_ outcome not consistent with get_document_info output HOT 3
- `transform` method not handling single embeddings or strings given to it. HOT 1
- results of `transform` is differnet from merged topic model `get_topic_info()` output HOT 1
- c_tf_idf_.indptr is None when attempting to save merged model HOT 6
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