Hi, I am working on a intent classification problem, so I am fine tuning bert for it,
here is my code:-
import random
import numpy as np

# This training code is based on the `run_glue.py` script here:
# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128

# Set the seed value all over the place to make this reproducible.
seed_val = 42

random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)

# We'll store a number of quantities such as training and validation loss, 
# validation accuracy, and timings.
training_stats = []

# Measure the total training time for the whole run.
total_t0 = time.time()

# For each epoch...
for epoch_i in range(0, epochs):
    
    # ========================================
    #               Training
    # ========================================
    
    # Perform one full pass over the training set.

    print("")
    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
    print('Training...')

    # Measure how long the training epoch takes.
    t0 = time.time()

    # Reset the total loss for this epoch.
    total_train_loss = 0

    # Put the model into training mode. Don't be mislead--the call to 
    # `train` just changes the *mode*, it doesn't *perform* the training.
    # `dropout` and `batchnorm` layers behave differently during training
    # vs. test (source: https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch)
    model.train()

    # For each batch of training data...
    for step, batch in enumerate(train_dataloader):

        # Progress update every 40 batches.
        if step % 40 == 0 and not step == 0:
            # Calculate elapsed time in minutes.
            elapsed = format_time(time.time() - t0)
            
            # Report progress.
            print('  Batch {:>5,}  of  {:>5,}.    Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))

        # Unpack this training batch from our dataloader. 
        #
        # As we unpack the batch, we'll also copy each tensor to the GPU using the 
        # `to` method.
        #
        # `batch` contains three pytorch tensors:
        #   [0]: input ids 
        #   [1]: attention masks
        #   [2]: labels 
        b_input_ids = batch[0].to(device)
        b_input_mask = batch[1].to(device)
        b_labels = batch[2].to(device)

        # Always clear any previously calculated gradients before performing a
        # backward pass. PyTorch doesn't do this automatically because 
        # accumulating the gradients is "convenient while training RNNs". 
        # (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
        model.zero_grad()        

        # Perform a forward pass (evaluate the model on this training batch).
        # The documentation for this `model` function is here: 
        # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
        # It returns different numbers of parameters depending on what arguments
        # arge given and what flags are set. For our useage here, it returns
        # the loss (because we provided labels) and the "logits"--the model
        # outputs prior to activation.
        loss, logits = model(b_input_ids, 
                             token_type_ids=None, 
                             attention_mask=b_input_mask, 
                             labels=b_labels)

        # Accumulate the training loss over all of the batches so that we can
        # calculate the average loss at the end. `loss` is a Tensor containing a
        # single value; the `.item()` function just returns the Python value 
        # from the tensor. 
        print(loss)
        total_train_loss += loss.item()

        # Perform a backward pass to calculate the gradients.
        loss.backward()

        # Clip the norm of the gradients to 1.0.
        # This is to help prevent the "exploding gradients" problem.
        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

        # Update parameters and take a step using the computed gradient.
        # The optimizer dictates the "update rule"--how the parameters are
        # modified based on their gradients, the learning rate, etc.
        optimizer.step()

        # Update the learning rate.
        scheduler.step()

    # Calculate the average loss over all of the batches.
    avg_train_loss = total_train_loss / len(train_dataloader)            
    
    # Measure how long this epoch took.
    training_time = format_time(time.time() - t0)

    print("")
    print("  Average training loss: {0:.2f}".format(avg_train_loss))
    print("  Training epcoh took: {:}".format(training_time))
        
    # ========================================
    #               Validation
    # ========================================
    # After the completion of each training epoch, measure our performance on
    # our validation set.

    print("")
    print("Running Validation...")

    t0 = time.time()

    # Put the model in evaluation mode--the dropout layers behave differently
    # during evaluation.
    model.eval()

    # Tracking variables 
    total_eval_accuracy = 0
    total_eval_loss = 0
    nb_eval_steps = 0

    # Evaluate data for one epoch
    for batch in validation_dataloader:
        
        # Unpack this training batch from our dataloader. 
        #
        # As we unpack the batch, we'll also copy each tensor to the GPU using 
        # the `to` method.
        #
        # `batch` contains three pytorch tensors:
        #   [0]: input ids 
        #   [1]: attention masks
        #   [2]: labels 
        b_input_ids = batch[0].to(device)
        b_input_mask = batch[1].to(device)
        b_labels = batch[2].to(device)
        
        # Tell pytorch not to bother with constructing the compute graph during
        # the forward pass, since this is only needed for backprop (training).
        with torch.no_grad():        

            # Forward pass, calculate logit predictions.
            # token_type_ids is the same as the "segment ids", which 
            # differentiates sentence 1 and 2 in 2-sentence tasks.
            # The documentation for this `model` function is here: 
            # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
            # Get the "logits" output by the model. The "logits" are the output
            # values prior to applying an activation function like the softmax.
            (loss, logits) = model(b_input_ids, 
                                   token_type_ids=None, 
                                   attention_mask=b_input_mask,
                                   labels=b_labels)
            
        # Accumulate the validation loss.
        total_eval_loss += loss.item()

        # Move logits and labels to CPU
        logits = logits.detach().cpu().numpy()
        label_ids = b_labels.to('cpu').numpy()

        # Calculate the accuracy for this batch of test sentences, and
        # accumulate it over all batches.
        total_eval_accuracy += flat_accuracy(logits, label_ids)
        

    # Report the final accuracy for this validation run.
    avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)
    print("  Accuracy: {0:.2f}".format(avg_val_accuracy))

    # Calculate the average loss over all of the batches.
    avg_val_loss = total_eval_loss / len(validation_dataloader)
    
    # Measure how long the validation run took.
    validation_time = format_time(time.time() - t0)
    
    print("  Validation Loss: {0:.2f}".format(avg_val_loss))
    print("  Validation took: {:}".format(validation_time))

    # Record all statistics from this epoch.
    training_stats.append(
        {
            'epoch': epoch_i + 1,
            'Training Loss': avg_train_loss,
            'Valid. Loss': avg_val_loss,
            'Valid. Accur.': avg_val_accuracy,
            'Training Time': training_time,
            'Validation Time': validation_time
        }
    )

print("")
print("Training complete!")

print("Total training took {:} (h:mm:ss)".format(format_time(time.time()-total_t0)))


I am getting a error:-
AttributeError                            Traceback (most recent call last)
<ipython-input-27-a6f23d2754c8> in <module>()
     92         # from the tensor.
     93         print(loss)
---> 94         total_train_loss += loss.item()
     95 
     96         # Perform a backward pass to calculate the gradients.

AttributeError: 'str' object has no attribute 'item'