Prediction with CRF for true test data

[ItikaGupta]

Hi,

Thank you for sharing the code for your work. I have been able to run the code and train the models for all the three cases. When I try to run predictor.py, it works fine for BERT and (BERT + Transformer) models. However, when using predictor.py for a (BERT + Transformer + CRF) model, since we don't have true labels for test data, the below code gives error:

  ` if self.with_crf:

        mask_sentences = (labels != -1)

        best_paths = self.crf.viterbi_tags(label_logits, mask_sentences)`

I understand why it gives an error, but was not sure how or what to modify in order for it to work.

Any suggestion will be really helpful.

Thank you!

[ItikaGupta]

I solved it by using sentence's bert tensors to create the label masks.