Add binary embedding quantization support to FeatureExtraction pipeline #691
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
xenova
reviewed
Apr 10, 2024
xenova
reviewed
Apr 10, 2024
xenova
reviewed
Apr 10, 2024
xenova
reviewed
Apr 10, 2024
|
Great job! Thanks for the PR! 🔥 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR addressed the below issue, using the examples from @xenova.
Closes [feat] Add binary & scalar embedding quantization support to Transformers.js #681
Let me know if you'd like any changes to this, perhaps we can make another options object in javascript like a QuantizationOptions so it's obvious which parameters are grouped together logically
I also added a texts === undefined check since I ran into an issue earlier where I was accidentally not passing in any texts and was met with an unfriendly error.
For some reason though I don't get the same similarity numbers as Xenova did on his example but the numbers are at least similar
It may also be beneficial to bring in https://github.com/ashvardanian/SimSIMD for this but haven't ran any benchmarks within browser context yet