no code implementations • 27 Sep 2023 • Zihao Deng, Benjamin Ghaemmaghami, Ashish Kumar Singh, Benjamin Cho, Leo Orshansky, Mattan Erez, Michael Orshansky
At constant model quality, MLET allows embedding dimension, and model size, reduction by up to 16x, and 5. 8x on average, across the models.
no code implementations • 28 Mar 2022 • Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli, Dmitriy Korchev, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov
However, this approach introduces collisions between semantically dissimilar ids that degrade model quality.
no code implementations • 10 Jun 2020 • Benjamin Ghaemmaghami, Zihao Deng, Benjamin Cho, Leo Orshansky, Ashish Kumar Singh, Mattan Erez, Michael Orshansky
Increasing the dimension of embedding vectors improves model accuracy but comes at a high cost to model size.