Pytorch: IndexError: index out of range in self. How to solve?
This training code is based on the run_glue.py script found here:
# 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)

# Store the average loss after each epoch so we can plot them.
loss_values = []

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

    print(&quot;&quot;)
    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_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 100 batches.
        if step % 100 == 0 and not step == 0:
            # Calculate elapsed time in minutes.
            elapsed = format_time(time.time() - t0)
            
            # Report progress.
            print('  Batch {:&gt;5,}  of  {:&gt;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 &quot;convenient while training RNNs&quot;. 
        # (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).
        # This will return the loss (rather than the model output) because we
        # have provided the `labels`.
        # The documentation for this `model` function is here: 
        # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
        outputs = model(b_input_ids, 
                    token_type_ids=None, 
                    attention_mask=b_input_mask, 
                    labels=b_labels)
        
        # The call to `model` always returns a tuple, so we need to pull the 
        # loss value out of the tuple.
        loss = outputs[0]

        # 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.
        total_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 &quot;exploding gradients&quot; 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 &quot;update rule&quot;--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 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(&quot;&quot;)
    print(&quot;  Average training loss: {0:.2f}&quot;.format(avg_train_loss))
    print(&quot;  Training epcoh took: {:}&quot;.format(format_time(time.time() - t0)))
        
    # ========================================
    #               Validation
    # ========================================
    # After the completion of each training epoch, measure our performance on
    # our validation set.

    print(&quot;&quot;)
    print(&quot;Running Validation...&quot;)

    t0 = time.time()

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

    # Tracking variables 
    eval_loss, eval_accuracy = 0, 0
    nb_eval_steps, nb_eval_examples = 0, 0

    # 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
        
        # Telling the model not to compute or store gradients, saving memory and
        # speeding up validation
        with torch.no_grad():        

            # Forward pass, calculate logit predictions.
            # This will return the logits rather than the loss because we have
            # not provided labels.
            # token_type_ids is the same as the &quot;segment ids&quot;, 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
            outputs = model(b_input_ids, 
                            token_type_ids=None, 
                            attention_mask=b_input_mask)
        
        # Get the &quot;logits&quot; output by the model. The &quot;logits&quot; are the output
        # values prior to applying an activation function like the softmax.
        logits = outputs[0]

        # 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.
        tmp_eval_accuracy = flat_accuracy(logits, label_ids)
        
        # Accumulate the total accuracy.
        eval_accuracy += tmp_eval_accuracy

        # Track the number of batches
        nb_eval_steps += 1

    # Report the final accuracy for this validation run.
    print(&quot;  Accuracy: {0:.2f}&quot;.format(eval_accuracy/nb_eval_steps))
    print(&quot;  Validation took: {:}&quot;.format(format_time(time.time() - t0)))

print(&quot;&quot;)
print(&quot;Training complete!&quot;)

The error is as follows, while running the training for text classification using bert models came across the follow.
    ~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/sparse.py in forward(self, input)
    112         return F.embedding(
    113             input, self.weight, self.padding_idx, self.max_norm,
--&gt; 114             self.norm_type, self.scale_grad_by_freq, self.sparse)
    115 
    116     def extra_repr(self):

~/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   1722         # remove once script supports set_grad_enabled
   1723         _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-&gt; 1724     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
   1725 
   1726 

IndexError: index out of range in self

How can I fix it?

I think you have messed up with input dimension declared torch.nn.Embedding and with your input. torch.nn.Embedding is a simple lookup table that stores embeddings of a fixed dictionary and size.
Any input less than zero or more than declared input dimension raise this error.
Compare your input and the dimension mentioned in torch.nn.Embedding.
Attached code snippet to simulate the issue.
from torch import nn
input_dim = 10
embedding_dim = 2
embedding = nn.Embedding(input_dim, embedding_dim)
err = True
if err:
    #Any input more than input_dim - 1, here input_dim = 10
    #Any input less than zero
    input_to_embed = torch.tensor([10])
else:
    input_to_embed = torch.tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
embed = embedding(input_to_embed)
print(embed)

Hope this will solve your issue.
