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.

介绍了一个名为 Universal NER (UNER) 的开源项目，旨在开发多种语言的黄金标准 NER 基准；UNER 的目标是提供高质量的、跨语言一致的标注，以促进和标准化多语言 NER 研究。UNER v1 包含使用一致的跨语言模式在 12 种不同语言中注释的 18 个数据集。在本文中，我们详细介绍了 UNER 的数据集创建和组成，并提供了在本语言和跨语言学习设置下的初始建模基线。我们向公众发布了数据、代码和拟合模型。