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Thanks for your great work and datasets! I wonder how you treated label blank while training the model? As the labeler model should output only positive, negative and uncertain. Also did you use any tricks trying to fix the unbalanced dataset like data augmentation or twisted training loss functions to make model less biased?
The text was updated successfully, but these errors were encountered:
Thanks for your great work and datasets! I wonder how you treated label blank while training the model? As the labeler model should output only positive, negative and uncertain. Also did you use any tricks trying to fix the unbalanced dataset like data augmentation or twisted training loss functions to make model less biased?
Thanks for the kind words! Blank label was treated as negative (no mention of the category in the report). We did not use any methods for class balancing in our final models - the full approach is described in our paper!
Thanks for your great work and datasets! I wonder how you treated label blank while training the model? As the labeler model should output only positive, negative and uncertain. Also did you use any tricks trying to fix the unbalanced dataset like data augmentation or twisted training loss functions to make model less biased?
The text was updated successfully, but these errors were encountered: