Fascinating - Binary vector embeddings and Matryoshka embeddings !

Imagine taking a massive book and somehow compressing it to a tiny pamphlet - but still keeping almost all the important information !

Binary vector embeddings achieve up to 32x compression while maintaining over 95% retrieval accuracy.

How ? It takes each of the 32-bit floating point weights in a vector embedding and converts them to a single bit. If the original weight was greater than 0, map it to a 1, otherwise map it to a 0.

This breakthrough dramatically reduces storage costs and computational overhead for machine learning applications.

Combining binary quantization with Matryoshka representation learning, researchers can now create embeddings as small as 1.56% of the original size while maintaining over 90% accuracy.

Reference - Binary vector embeddings are so cool

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