VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition
Computation and Language
2022-12-15 v1
Abstract
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.
Cite
@article{arxiv.2212.07219,
title = {VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition},
author = {Xuan-Dung Doan},
journal= {arXiv preprint arXiv:2212.07219},
year = {2022}
}