Ternary Weight Splitting

Ternary Weight Splitting: A New Approach for Training BinaryBERT Ternary Weight Splitting (TWS) is a novel approach to training natural language processing (NLP) models like BinaryBERT. BinaryBERT is a type of model that approximates regular BERT, a well-known architecture for fine-tuning NLP tasks. TWS is used to optimize the performance of BinaryBERT by exploiting the "flatness" of ternary loss landscapes. In this article, we will explore what TWS is, how it works, and why it is important.