Thanks for a great package. For a multi-label training problem, the input training data should like like this:
id | text | toxic | severe_toxic | obscene | threat | insult | identity_hate
0 | Why the edits made under my username Hardcore Metallica Fan were reverted? | 0 | 0 | 0 | 0 | 0 | 0
I would like to be able to add additional computed features like this:
id | text | f1 | f2 | f3 | toxic | severe_toxic | obscene | threat | insult | identity_hate
0 | Why the edits made under my username Hardcore Metallica Fan were reverted? | 0.37 | 45 | 1.8 | 0 | 0 | 0 | 0 | 0 | 0
How would I do this in fast-bert?
Cheers,
Noah
Hi,
Thanks for a great package. For a multi-label training problem, the input training data should like like this:
I would like to be able to add additional computed features like this: