Target size (torch.Size([16])) must be the same as input size (torch.Size([16, 5]))

Question

I am doing a text classification using pytorch and BERTSequenceClassification. The model will classify the text into 5 classes but I got this error while doing the training.

ValueError: Target size (torch.Size([16])) must be the same as input size (torch.Size([16, 5]))

num_epochs = 5
eval_every = len(train_loader) // 2
file_path = destination_folder
best_valid_loss = float("Inf")

# initialize running values
running_loss = 0.0
valid_running_loss = 0.0
global_step = 0
train_loss_list = []
valid_loss_list = []
global_steps_list = []

# training loop
model.train()
for epoch in range(num_epochs):
    for (labels, OriginalTweet), _ in train_loader:
      
        labels = labels.type(torch.LongTensor)
        labels = labels.to(device)   
        OriginalTweet = OriginalTweet.type(torch.LongTensor)  
        OriginalTweet = OriginalTweet.to(device)
        output = model(OriginalTweet, labels)
        loss, _ = output

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # update running values
        running_loss += loss.item()
        global_step += 1

        # evaluation step
        if global_step % eval_every == 0:
            model.eval()
            with torch.no_grad():                    

                # validation loop
                for (labels,OriginalTweet), _ in valid_loader:
                    labels = labels.type(torch.LongTensor)           
                    labels = labels.to(device)
                    OriginalTweet = OriginalTweet.type(torch.LongTensor)  
                    OriginalTweet = OriginalTweet.to(device)
                    output = model(OriginalTweet, labels)
                    loss, _ = output
                    
                    valid_running_loss += loss.item()

            # evaluation
            average_train_loss = running_loss / eval_every
            average_valid_loss = valid_running_loss / len(valid_loader)
            train_loss_list.append(average_train_loss)
            valid_loss_list.append(average_valid_loss)
            global_steps_list.append(global_step)

            # resetting running values
            running_loss = 0.0                
            valid_running_loss = 0.0
            model.train()

            # print progress
            print('Epoch [{}/{}], Step [{}/{}], Train Loss: {:.4f}, Valid Loss: {:.4f}'
                  .format(epoch+1, num_epochs, global_step, num_epochs*len(train_loader),
                          average_train_loss, average_valid_loss))
            
            # checkpoint
            if best_valid_loss > average_valid_loss:
                best_valid_loss = average_valid_loss
                save_checkpoint(file_path + '/' + 'model.pt', model, best_valid_loss)
                save_metrics(file_path + '/' + 'metrics.pt', train_loss_list, valid_loss_list, global_steps_list)

save_metrics(file_path + '/' + 'metrics.pt', train_loss_list, valid_loss_list, global_steps_list)

→ output = model(OriginalTweet, labels) above is the line that causes this error

I am very new to this field. Any help and advice is very appreciated. Thanks in advance!