jkrukowski/swift-embeddings
Run embedding models locally in Swift using MLTensor.
This tool helps Swift developers integrate powerful natural language processing capabilities directly into their applications. It takes raw text inputs and converts them into numerical 'embeddings' using various sophisticated models like BERT or CLIP, which can then be used for tasks like comparing text similarity or powering search. It's designed for Swift app developers building features that require understanding and processing human language on-device.
139 stars.
Use this if you are a Swift developer needing to incorporate advanced text understanding and comparison features into your app, directly on the user's device.
Not ideal if you are a data scientist or researcher working in Python or other languages, or if you need to perform image encoding with CLIP.
Stars
139
Forks
19
Language
Swift
License
MIT
Category
Last pushed
Feb 07, 2026
Commits (30d)
0
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curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/jkrukowski/swift-embeddings"
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