论文标题
NL4OPT竞赛子任务1：命名实体识别的整体培训语言模型
VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition
论文作者
论文摘要
我们提出了三种预先训练的语言模型（XLM-R，Bart和Deberta-V3），作为命名实体识别的上下文化嵌入的能力。我们的模型在测试集上取得了92.9％的F1得分，在NL4OPT竞赛子任务1中排名第五。
We propose a combined three pre-trained language models (XLM-R, BART, and DeBERTa-V3) as an empower of contextualized embedding for named entity recognition. Our model achieves a 92.9% F1 score on the test set and ranks 5th on the leaderboard at NL4Opt competition subtask 1.
