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

# trainer/pretrain.py

```python
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.