How to train Ner Model having 1 entity?
Can we Create a model using (bi lstm crf) where we only want to predict 1 entity.?


[edited] Yes, the trick I would use is to train a regular model and predict the top 10 most likely predictions for each sentence. This will give you the different possible labelings ordered by probability. The idea is to select the first labeling which contains a single entity, i.e. the most likely option after eliminating the predictions with several entities.


  In CRF States of the neighbors affect the current prediction so predicting 1 entity per sentence seems difficult specially with CRF?


There's a confusion here: this is true but the "neighbors" are not other entities, they are the other words of the sentence. This means that if a word belongs to an entity then the next word is more likely to belong to an entity as well (i.e. to be part of the same entity). So using CRF doesn't increase the probability to find several distinct entities in a sentence. However if all the target entities are single words then CRFs might not be needed (I'm not sure whether there would any better alternative though).


  if i Cannot achieve this with CRF can I use Bert to train a model having 1 entity per model?


I don't know the answer to this one but I doubt it's needed.