  The transformer architecture has revolutionized Natural Language Processing
(NLP) and other machine-learning tasks, due to its unprecedented accuracy.
However, their extensive memory and parameter requirements often hinder their
practical applications. In this work, we study the effect of tensor-train
decomposition to improve the accuracy and compress transformer vision-language
neural networks, namely BERT and ViT. We focus both on embedding-layer
compression and partial tensorization of neural networks (PTNN) through an
algorithmic approach. Our novel PTNN approach significantly improves the
accuracy of existing models by up to 5%, all without the need for post-training
adjustments, breaking new ground in the field of tensor decomposition.
