Masks in Meter.update()

[and-tos]

Hi,
I'm trying to use masks for multi task learning.
The documentation in eval.py about the use of masks is not clear to me.

mask : None or float32 tensor
            Binary mask indicating the existence of ground truth labels with
            shape ``(B, T)``. If None, we assume that all labels exist and create
            a one-tensor for placeholder.

If a mask is set to 1 it could mean that a) the label will be masked or b) the label is present. Which one is it?

If label = [5, None, None],
should I set mask = [1,0,0] or [0,1,1]

What's the convention if I wanted to exclude the "None" labels from the loss calculation?

Thanks a lot for clarifying!

[mufeili]

1 indicates the existence of a label. In your example, it will be [1, 0, 0]. For loss computation, we can multiply the prediction by the mask so that we will only update the model based on existing labels.