  Named entity recognition is a natural language processing task to recognize
and extract spans of text associated with named entities and classify them in
semantic Categories.
  Google BERT is a deep bidirectional language model, pre-trained on large
corpora that can be fine-tuned to solve many NLP tasks such as question
answering, named entity recognition, part of speech tagging and etc. In this
paper, we use the pre-trained deep bidirectional network, BERT, to make a model
for named entity recognition in Persian.
  We also compare the results of our model with the previous state of the art
results achieved on Persian NER. Our evaluation metric is CONLL 2003 score in
two levels of word and phrase. This model achieved second place in NSURL-2019
task 7 competition which associated with NER for the Persian language. our
results in this competition are 83.5 and 88.4 f1 CONLL score respectively in
phrase and word level evaluation.
