silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages
Sumit Kumar Singh, Pawankumar Jawale, Uma Tiwary · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
We present Transformer based pretrained models, which are fine-tuned for Named Entity Recognition (NER) task.Our team participated in SemEval-2022 Task 11 Multi-CoNER: Multilingual Complex Named Entity Recognition task for Hindi and Bangla.Result comparison of six models (mBERT, IndicBERT, MuRIL (Base), MuRIL (Large), XLM-RoBERTa (Base) and XLM-RoBERTa (Large) ) has been performed.It is found that among these models MuRIL (Large) model performs better for both the Hindi and Bangla languages.Its F1-Scores for Hindi and Bangla are 0.69 and 0.59 respectively.