why specify ignore_index=0 in the NLLLoss function in BERTTrainer?

[Jasmine969]

trainer/pretrain.py

class BERTTrainer:
    def __init__(self, ...):
        ... 
        # Using Negative Log Likelihood Loss function for predicting the masked_token
        self.criterion = nn.NLLLoss(ignore_index=0)
        ...

I cannot understand why ignore index=0 is specified when calculating NLLLoss. If the ground truth of is_next is False (label = 0) in terms of the NSP task but BERT predicts True, then NLLLoss will be 0 (or nan)... so what's the aim of ignore_index = 0 ???

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Well, I've found that ignore_index = 0 is useful to the MLM task, but I still can't agree the NSP task should share the same NLLLoss with MLM.

[LMC63]

see #32
change
self.criterion = nn.NLLLoss(ignore_index=0)
to
self.criterion = nn.NLLLoss()