  We introduce Universal NER (UNER), an open, community-driven project to
develop gold-standard NER benchmarks in many languages. The overarching goal of
UNER is to provide high-quality, cross-lingually consistent annotations to
facilitate and standardize multilingual NER research. UNER v1 contains 18
datasets annotated with named entities in a cross-lingual consistent schema
across 12 diverse languages. In this paper, we detail the dataset creation and
composition of UNER; we also provide initial modeling baselines on both
in-language and cross-lingual learning settings. We release the data, code, and
fitted models to the public.
