Constant output predictions on test data
Hi, am trying to train xlm-roberta-base on custom dataset. here is code for trainingloss_values = []
# number of total steps for each epoch
print('total steps per epoch: ',  len(train_dataloader) / batch_size)
# looping over epochs
for epoch_i in range(0, epochs):
    
    print('training on epoch: ', epoch_i)
    # set start time 
    t0 = time.time()
    # reset total loss
    total_loss = 0
    # model in training 
    model.train()
    # loop through batch 
    for step, batch in enumerate(train_dataloader):
        # Progress update every 50 step 
        if step % 50 == 0 and not step == 0:
            print('training on step: ', step)
            print('total time used is: {0:.2f} s'.format(time.time() - t0))
        # load data from dataloader 
        b_input_ids = batch[0].to(device)
        b_input_mask = batch[1].to(device)
        b_labels = batch[2].to(device)
        # clear any previously calculated gradients 
        model.zero_grad()
        # get outputs
        outputs = model(b_input_ids,
                        token_type_ids=None,
                        attention_mask=b_input_mask,
                        labels=b_labels)
        # get loss
        loss = outputs[0]
        # total loss
        total_loss += loss.item()
        # clip the norm of the gradients to 1.0.
        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
        # update optimizer
        optimizer.step()
        # update learning rate 
        scheduler.step()
    # Calculate the average loss over the training data.
    avg_train_loss = total_loss / len(train_dataloader)
    # Store the loss value for plotting the learning curve.
    loss_values.append(avg_train_loss)
    print("average training loss: {0:.2f}".format(avg_train_loss))every thing works fine but when i test this model on test data, it gives all label as 0t0 = time.time()
# model in validation mode
model.eval()
# save prediction
predictions,true_labels =[],[]
# evaluate data for one epoch
for batch in validation_dataloader:
    # Add batch to GPU
    batch = tuple(t.to(device) for t in batch)
    # Unpack the inputs from our dataloader
    b_input_ids, b_input_mask, b_labels = batch
    # validation
    with torch.no_grad():
        outputs = model(b_input_ids,
                        token_type_ids=None,
                        attention_mask=b_input_mask)
    # get output
    logits = outputs[0]
    # move logits and labels to CPU
    logits = logits.detach().cpu().numpy()
    label_ids = b_labels.to('cpu').numpy()
    final_prediction = np.argmax(logits, axis=-1).flatten()
    predictions.append(final_prediction)
    true_labels.append(label_ids)
    
print('total time used is: {0:.2f} s'.format(time.time() - t0))array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), …where as true labels arearray([1, 1, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0]),array([0, 1, 2, 0, 2, 2, 2, 0, 1, 1, 2, 2, 0, 1, 1, 2]),array([0, 0, 1, 0, 2, 2, 2, 0, 0, 0, 0, 1, 0, 2, 2, 2]),array([2, 0, 1, 0, 0, 1, 0, 1, 1, 2, 0, 1, 1, 2, 1, 0]),array([2, 0, 0, 0, 0, 1, 2, 0, 2, 1, 1, 1, 2, 1, 1, 2]),array([0, 1, 1, 1, 0, 1, 1, 1, 0, 2, 1, 1, 2, 2, 2, 2]),array([1, 1, 0, 0, 2, 0, 2, 0, 2, 1, 1, 1, 1, 0, 1, 0]),