Pawankumar Jawale

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2022

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silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages
Sumit Singh | Pawankumar Jawale | Uma Tiwary
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-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 MultiCoNER: 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.
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