Hi!
I’m looking to fine-tune an NER model (dslim/bert-base-NER-uncased) with my own data.
My annotations are of this form: for each example I have a piece of raw text (str) and a list of annotated spans of this form: {start_index: int, end_index: int, tag: str}
However, to fine-tune the NER model, I need to prepare X (tokens) and Y (token tags) for each example. So, those spans have to be translated into token tags, matching the model’s tokenizer.
Hope that makes sense.
Is there a way to handle this? Or, what would you recommend?
Thanks!