The labels I want to predict are the followings:
date, job title, company name, job description and O label for the tokens which are not associated with these labels.
In few words, i would like to find a way to label a job description text with just one label (and not one label for each token of job description text) while at same time to label each token of the text that is not part of the job description text.
The problem is that the job description text and the job title/date/company name text are in the same block of text and I have to use just one model.
Thinking about it, I do not think this is possible, so I think I will perform a NER task for the job description text too (one label for each token of job description text).
By the way, I have a question.
Did you executed some test or do you have some reference which shows if BERT model can capture well the semantic information of a text block of 512 words? I’m asking this because I know that the longer the text, the more difficult it is to capture its semantic content and I think that 512 tokens are quite a lot.
In addition, in my case I can’t do a truncation because I need all the information of the Work experience text, so I think that your doubt depends on the context in which you are working on