Comments (2)
I'm not sure but I believe you have to set the numpy version even lower which is by no means an ideal solution. Instead, it might be worthwhile to use zero-shot BERTopic instead.
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Thank you for the reply.
I have previously tried setting both numpy to 1.23.5 and numba to 0.56.0, but it gave me an error:
numba 0.56.0 requires numpy<1.23,>=1.18, but you have numpy 1.23.5 which is incompatible.
As for numba 0.56.4 it still produced the same error as the original post.
I'll have a look at zero-shot approach.
Thank you once again!
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Related Issues (20)
- Supervised topic model generating different topics to training data HOT 3
- Where is the full data set of embeddings? HOT 3
- Visualization in html page HOT 1
- Utilizing the GPU of MacBook Pro M3 to accelerate the process of fit_transform HOT 1
- Could we know the weights of each topic? HOT 6
- Can't reproduce same results when using cuml version of UMAP and HDBSCAN HOT 3
- 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
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