  In this paper, we present an ensemble approach for the NL4Opt competition
subtask 1(NER task). For this task, we first fine tune the pretrained language
models based on the competition dataset. Then we adopt differential learning
rates and adversarial training strategies to enhance the model generalization
and robustness. Additionally, we use a model ensemble method for the final
prediction, which achieves a micro-averaged F1 score of 93.3% and attains the
second prize in the NER task.
