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silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages

SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop (2022) 1536-1542
DOI: 10.18653/v1/2022.semeval-1.211
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Abstract

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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APA

Jawale, P., Singh, S., & Tiwary, U. S. (2022). silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages. In SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop (pp. 1536–1542). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.semeval-1.211

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