I am training a spacy model from scratch by creating a dataset of my own with format spacy needs it to be in, the model is an NER model and the entity i am trying to recognize is Food items. I have created a dataset with 263 rows and after training the spacy model from scratch on this dataset, my model is performing great (I am getting around 80% accuracy) and this accuracy may not look a lot but it is better and I am being able to do my task a lot better now.
Now I want to improve my model even further by increasing train data. For increasing the train data I am thinking of using the rows I am sending as test and checking manually if spacy recognised each entity correctly or not and if all entities in a sentence are recognised correctly use this sentence in my training set.
My question is will this method improve my model in any way ? Or in general if we add data which is correctly classified by our model in training set, will the performance of model improve to some extent?