Abstract
Abstract: In our current era where technology keeps increasing, where data driven applications are reign supreme, Mamed Entity Recognition (NER) emerges as an important tool, aiding in better understanding of text and powering various natural language processing tasks downstream. This paper takes into the use of pre trained BERT model for NER tasks, Skipping the need for further adjustments. Our aim is to boost the accuracy and speed of NER systems by leveraging BERT’s pre trained representations. Our approach involves leveraging built in capabilities to spot key entity labels like names, dates, and people, streaming the NER process while achieving competitive results. This research takes a step forward in maximizing the potential of pre trained models for NER applications ultimately making text analysis easier and more efficient in today’s digital world.
Published Version
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