Examples : I have a sentence in job description : "Java senior engineer in UK ".
I want to use a deep learning model to predict it as 2 categories :
IT jobs. If i use traditional classification model, it only can predict 1 label with
softmax function at last layer . Thus, i can use 2 model neural networks to predict "Yes"/"No" with both categories, but if we have more categories, it is too expensive . So do we have any deeplearning or machine learning model to predict 2 or more categories at same time ?
"Edit" : With 3 labels by traditional approach , it will be encoded by [1,0,0] but in my case, it will be encoded by [1,1,0] or [1,1,1]
Example : if we have 3 labels, and a sentences may be fit with all of these labels. So if output from softmax function is [0.45 , 0.35 , 0.2 ] we should classify it into 3 labels or 2 labels , or may be one ? the main problem when we do it is : what is good threshold to classify into 1, or 2 , or 3 labels ?