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We propose a combined three pre-trained language models (XLM-R, BART, andDeBERTa-V3) as an empower of contextualized embedding for named entityrecognition. Our model achieves a 92.9% F1 score on the test set and ranks 5thon the leaderboard at NL4Opt competition subtask 1.

VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition