Hi !
I’m new to NLP and transformers. I have readen documentation on this (BERT, Attention is All you need, …).
I’m trying to implement a Bert model that I want to fine tune which compute two different classifications :

first one : 2 classes
second classifier : 5 classes

I have a my labels as :
\y : 

tensor([[[[1],
          [0]]],


        [[[1],
          [0]]]])

The outputs after model(x) :
Output : 

[tensor([[ 0.1207, -0.2359],
        [ 0.4030, -0.0475]], grad_fn=<AddmmBackward>), tensor([[ 0.1071,  0.1679,  0.0090, -0.7056, -0.1793],
        [ 0.1295, -0.0781,  0.3041, -0.6385,  0.1090]],
       grad_fn=<AddmmBackward>)]


As you can see, I have 2 classifier of size 2 and 5. and the labels corresponds to the index of each classifier output.
If I apply criterion as :
for i in range(2) : #for each label
     loss+= criterion(output[i],y[i])

For i = 0, I print these two output[0] and y[0] :
output[i] :

tensor([[ 0.4083, -0.1396],
        [ 0.4059, -0.3079]], grad_fn=<AddmmBackward>)
y[i] :

tensor([[[1],
         [1]]])

I got the error :
ValueError: Expected input batch_size (2) to match target batch_size (1).
I’m stuck on this and I have the impression of forbid something about y format but I’m not sure. Someone could help me on this one ? Thanks you