This study examines transformer-based models and their effectiveness in named
entity recognition tasks. The study investigates data representation
strategies, including single, merged, and context, which respectively use one
sentence, multiple sentences, and sentences joined with attention to context
per vector. Analysis shows that training models with a single strategy may lead
to poor performance on different data representations. To address this
limitation, the study proposes a combined training procedure that utilizes all
three strategies to improve model stability and adaptability. The results of
this approach are presented and discussed for four languages (English, Polish,
Czech, and German) across various datasets, demonstrating the effectiveness of
the combined strategy.

该研究通过研究基于 Transformer 的模型及其在命名实体识别任务中的有效性，探讨了数据表示策略，包括单句、多句和上下文，并分析了使用单一策略训练模型可能导致在不同数据表示上表现不佳的问题。为了解决这个局限性，该研究提出了一种结合训练过程，利用这三种策略来提高模型的稳定性和适应性。该方法的结果在四种语言（英语、波兰语、捷克语和德语）以及各种数据集上进行了展示和讨论，证明了这种结合策略的有效性。