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Hi,
Very nice work!
How to generate visual image of 'Similarity of Attention Outputs' as shown in Figure 4?
Could the author share the code for visualization??
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### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a sim…
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For those who get variables error for 'docs_example', please rename the documents into 'docs_example'.
And please add `from sklearn.decomposition import NMF` for the NMF part
and `from sklearn…
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Just noting that this function to create neighbourhood land cover-based weights uses `dist()` with default options. This is a Euclidean distance measure that is potentially inappropriate for very spar…
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Dear Creator of the amazing BERTopic
I want to perform cosine similarity of the topic_embeddings to a list of labels. I found it to perform better than zeroshot (and faster !) for my use case. Howe…
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Hi,
I was wondering if the semantic search would improve if one would train a multilabel-classification model and use those embeddings?
After training a binary classification model I have seen t…
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While the `cosine_similarity` method works effectively for most vectors, there's a potential edge case that could lead to a division by zero error. (I agree that it's a rare case, but we can handle it…
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We should document somewhere what the score means; what is its range? From -1 to 1? Is it cosine similarity? Probably in the docstring on the search endpoint
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The new added code has some issue:
` # Distance based on cosine similarity`
` dot = np.sum(np.multiply(embeddings1, embeddings2), axis=1)`
` norm = np.linalg.norm(embeddings1…
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- Update existing cosine similarity model
- User's skills must be matched to all courses' skills and similarity score must be computed