Improvement of cosine similarity
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- Dominant language
- Python
- Stars
- 12.5k
- Forks
- 991
- Avg merge
- 3d 13h
- Merged PRs (30d)
- 10
Description
I saw that currently the cosine similarity is computed on a vector by vector basis, which can become quite slow for a big amount of samples.
I recently had the same issue on a personal project with around 9k embeddings; I ended up using sklearn's pairwise cosine similarity, and that was much faster as it computes the whole matrix at once.
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
Locate the current vector-by-vector cosine similarity implementation and inspect how embeddings are passed through it. Compare its results and performance with scikit-learn's pairwise cosine similarity on a large embedding set; done means preserving expected similarity values while improving performance for many samples.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100