This paper introduces the Swedish BERT ("KB-BERT") developed by the KBLab for
data-driven research at the National Library of Sweden (KB). Building on recent
efforts to create transformer-based BERT models for languages other than
English, we explain how we used KB's collections to create and train a new
language-specific BERT model for Swedish. We also present the results of our
model in comparison with existing models - chiefly that produced by the Swedish
Public Employment Service, Arbetsf\"ormedlingen, and Google's multilingual
M-BERT - where we demonstrate that KB-BERT outperforms these in a range of NLP
tasks from named entity recognition (NER) to part-of-speech tagging (POS). Our
discussion highlights the difficulties that continue to exist given the lack of
training data and testbeds for smaller languages like Swedish. We release our
model for further exploration and research here:
this https URL .

本文介绍了由 KBLab 开发的瑞典 BERT（“KB-BERT”），并对其进行了性能比较，证明其在命名实体识别和词性标注等自然语言处理任务中的表现优于瑞典公共就业服务的 BERT 和 Google 的多语言 M-BERT。我们释放了我们的模型进行进一步的探索和研究。