Validation in CRF mode

[furkanpala]

Hi, thanks for your effort.

I noticed a problem in the pipeline.train_val_utils.validate when we are running on crf classifier mode such that the inference function the crf.py returns the predicted target sequence (which is actually predicted class ids), not the probabilities for each class. Then, pipeline.train_val_utils.validate function, we are getting the predicted class ids by running torch.argmax. However, as I said, we do not have the probailities here, instead we directly have the predicted class ids.

I think we can solve this by adding a is_crf argument in the pipeline.train_val_utils.validate function and an if block decides to apply the argmax or not.

Also, irrelevant but there is a type here

ViBERTgrid-PyTorch/model/field_type_classification_head.py

Line 474 in 97af769

if layer_mode == "sigle":

Thanks, sincerely.

[ZeningLin]

Hi~

When using the crf decoder, the seqeval library will be used as the evaluation metric, in this case only the predicted class is needed. Maybe a restriction should be added in the validation step. I will work on it later~

[furkanpala]

Thanks...