AttributeError: 'str' object has no attribute 'item' - Bert Fine tuning
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'
2022-01-19T06:57:37Z
Here is the collab:-Google Colab
2022-01-19T13:44:29Z
You can’t writeloss, logits = model(...)since v4, as the output of the model is a dictionary, so this will give you the keys (hence the fact your loss is a string) and not the values.You can do:loss, logits = model(...).to_tuple()oroutputs = model(...)
loss = outputs.loss
logits = outputs.logits
2022-01-19T19:09:39Z
Thanks,@sgugger,I am having another question, I have trained my model, Now I trained as well, Now I need to make predictions,sents = ["how would you say fly in italian"]


encoded_dicts = tokenizer.encode_plus(
                        sents,                      # Sentence to encode.
                        add_special_tokens = True, # Add '[CLS]' and '[SEP]'
                        max_length = 64,           # Pad & truncate all sentences.
                        pad_to_max_length = True,
                        return_attention_mask = True,   # Construct attn. masks.
                        return_tensors = 'pt',     # Return pytorch tensors.
                   )
    
    # Add the encoded sentence to the list.    
one_in = encoded_dicts['input_ids'].to(device)
    
    # And its attention mask (simply differentiates padding from non-padding).
atten_in = encoded_dicts['attention_mask'].to(device)
outputs = model(one_in, token_type_ids=None, 
                attention_mask=atten_in)

logits = outputs[0]
logits = logits.detach().cpu().numpy()I am using above code to make predictions, How do I know which is my category as I have 151 categories to be predicted, I used LabelEncoder to encode the intent column, so how do I decide which Intent it is?image680×555 22.4 KBColab is here:-Google ColabI have another question, I was using PyTorch Lightning, so below is the code,import numpy as np
import pandas as pd

from tqdm import tqdm
import torch
from transformers import BertTokenizer
from torch.utils.data import Dataset, DataLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks.early_stopping import EarlyStopping

from transformers import BertForSequenceClassification, AdamW, BertConfigI am gettingImportError: cannot import name 'TFPreTrainedModel' from 'transformers' (E:\Anaconda\lib\site-packages\transformers\__init__.py)
2022-01-20T19:14:42Z
@sguggerCan u take a look at the problem, It is not yet solved!