  Supervised models trained to predict properties from representations have
been achieving high accuracy on a variety of tasks. For instance, the BERT
family seems to work exceptionally well on the downstream task from NER tagging
to the range of other linguistic tasks. But the vocabulary used in the medical
field contains a lot of different tokens used only in the medical industry such
as the name of different diseases, devices, organisms, medicines, etc. that
makes it difficult for traditional BERT model to create contextualized
embedding. In this paper, we are going to illustrate the System for Named
Entity Tagging based on Bio-Bert. Experimental results show that our model
gives substantial improvements over the baseline and stood the fourth runner up
in terms of F1 score, and first runner up in terms of Recall with just 2.21 F1
score behind the best one.
