Need help training your model? See our top tips below for each stage of the training process:
The two most common pitfalls to watch out for when you first label a dataset are:
|Applying labels inconsistently. Make sure you apply it to all occasions it should apply and making sure you don't change your definition of what the label means part way through labelling. If you do you should go back and review where you have labeled it before|
|Partial labelling and not applying all labels that apply to a verbatim. When applying labels to a verbatim make sure you apply all labels that should be applied and not just the one you are focusing on. By not doing this you are telling the model that other labels don't apply|
Applying Labels inconsistently
Below shows some verbatims taken from dummy hotel reviews where there is a label for the size of the room, called 'Room > Size'. The first two images show verbatims where this label should be applied but the user has not applied them consistently:
Figure 1: Verbatim with the 'Room > Size' label applied correctly
Figure 2: Verbatim with the 'Room > Size' label not applied when it should be
In the example above, the 'Room' label has been applied to the second image but not the 'Room > Size' label, where it should have. This is inconsistent with the first example and will confuse the model because in the second example you are telling the model that the 'Room > Size' label does not apply when it should.
Figure 3: Example showing a Verbatim that has been partially labelled.
In the example above, the user has not applied the label 'Room > Cleanliness' Label, even though it is clearly applicable and has been applied to similar Verbatims elsewhere. This is an example of partial labelling and users should ensure they add all labels that apply to a Verbatim.