  This paper describes RETVec, an efficient, resilient, and multilingual text
vectorizer designed for neural-based text processing. RETVec combines a novel
character encoding with an optional small embedding model to embed words into a
256-dimensional vector space. The RETVec embedding model is pre-trained using
pair-wise metric learning to be robust against typos and character-level
adversarial attacks. In this paper, we evaluate and compare RETVec to
state-of-the-art vectorizers and word embeddings on popular model architectures
and datasets. These comparisons demonstrate that RETVec leads to competitive,
multilingual models that are significantly more resilient to typos and
adversarial text attacks. RETVec is available under the Apache 2 license at
https://github.com/google-research/retvec.
