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.

翻译：本研究探讨了基于Transformer的模型及其在命名实体识别任务中的有效性。研究调查了数据表示策略，包括单一、合并和上下文策略，这些策略分别使用单个句子、多个句子以及结合上下文注意力机制的句子向量。分析表明，采用单一策略训练模型可能导致在不同数据表示上的性能不佳。为克服这一局限，本研究提出一种组合训练方法，利用全部三种策略以提升模型的稳定性和适应性。该方法在多种数据集上对四种语言（英语、波兰语、捷克语和德语）的实验结果进行了展示与讨论，验证了组合策略的有效性。