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Masks in Meter.update() #143
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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. |
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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.
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!
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