I am Creating a Custom NER (named entity recognition ) Model using bi directional LSTM and CRF.
During Study on Ner i see all example includes Multiple entities per sentence. For eample this sentence includes 2 entities
(jhon lives in Us) jhon = S-Per , US=S-Country
Can we Create a model using (bi lstm crf) where we only want to predict 1 entity.?
In CRF States of the neighbors affect the current prediction so predicting 1 entity per sentence seems difficult specially with CRF?
Question 3 :
if i Cannot achieve this with CRF can I use Bert to train a model having 1 entity per model?
Thanks In advance.