Thanks for the great package. I was training a model with the default settings, which went great. I then switched to a differentiable head, which crashed because I used string labels and didn't specify the number of classes. My bad! I should've read the docs.
However, I think it would be nice if it crashed immediately, and not after the fine-tuning stage. I think the dataset parameters could be checked, or even converted automatically:
We could check the number of classes, and hence set the output layer dimensionality automatically. (unless the differentiable head is created before the call to .train())
We could definitely convert the labels to integers automatically, which is exactly what a sklearn LogisticRegression ends up doing under the hood.
In the worst case, it would be nice to just throw a ValueError before starting training if the labels/classes aren't correct (i.e., the number of classes doesn't match or the labels are strings.)
I'd definitely be willing to do a PR, but only if you think it makes sense.
Hi,
Thanks for the great package. I was training a model with the default settings, which went great. I then switched to a differentiable head, which crashed because I used string labels and didn't specify the number of classes. My bad! I should've read the docs.
However, I think it would be nice if it crashed immediately, and not after the fine-tuning stage. I think the dataset parameters could be checked, or even converted automatically:
.train())ValueErrorbefore starting training if the labels/classes aren't correct (i.e., the number of classes doesn't match or the labels are strings.)I'd definitely be willing to do a PR, but only if you think it makes sense.