  On the world wide web, toxic content detectors are a crucial line of defense
against potentially hateful and offensive messages. As such, building highly
effective classifiers that enable a safer internet is an important research
area. Moreover, the web is a highly multilingual, cross-cultural community that
develops its own lingo over time. As such, it is crucial to develop models that
are effective across a diverse range of languages, usages, and styles. In this
paper, we present the fundamentals behind the next version of the Perspective
API from Google Jigsaw. At the heart of the approach is a single multilingual
token-free Charformer model that is applicable across a range of languages,
domains, and tasks. We demonstrate that by forgoing static vocabularies, we
gain flexibility across a variety of settings. We additionally outline the
techniques employed to make such a byte-level model efficient and feasible for
productionization. Through extensive experiments on multilingual toxic comment
classification benchmarks derived from real API traffic and evaluation on an
array of code-switching, covert toxicity, emoji-based hate, human-readable
obfuscation, distribution shift, and bias evaluation settings, we show that our
proposed approach outperforms strong baselines. Finally, we present our
findings from deploying this system in production.
