TypeError: linear(): argument 'input' (position 1) must be Tensor, not str
so ive been trying to work on some example of bert that i found on github as its the first time im trying to use bert and see how it works. The respiratory im working with is the following: https://github.com/prateekjoshi565/Fine-Tuning-BERT/blob/master/Fine_Tuning_BERT_for_Spam_Classification.ipynb
im using a different dataset however im getting the issue TypeError: linear(): argument 'input' (position 1) must be Tensor, not str&quot; and honestly i dont know what im doing wrong. is there anyone that could help me?
the code ive been using is the following:
# convert class weights to tensor
weights= torch.tensor(class_wts,dtype=torch.float)
weights = weights.to(device)

# loss function
cross_entropy  = nn.NLLLoss(weight=weights) 

# number of training epochs
epochs = 10

def train():
  
  model.train()

  total_loss, total_accuracy = 0, 0
  
  # empty list to save model predictions
  total_preds=[]
  
  # iterate over batches
  for step,batch in enumerate(train_dataloader):
    
    # progress update after every 50 batches.
    if step % 50 == 0 and not step == 0:
      print('  Batch {:&gt;5,}  of  {:&gt;5,}.'.format(step, len(train_dataloader)))

    # push the batch to gpu
    batch = [r.to(device) for r in batch]
 
    sent_id, mask, labels = batch

    # clear previously calculated gradients 
    model.zero_grad()        

    # get model predictions for the current batch
    preds = model(sent_id, mask)

    # compute the loss between actual and predicted values
    loss = cross_entropy(preds, labels)

    # add on to the total loss
    total_loss = total_loss + loss.item()

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

    # clip the the gradients to 1.0. It helps in preventing the exploding gradient problem
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

    # update parameters
    optimizer.step()

    # model predictions are stored on GPU. So, push it to CPU
    preds=preds.detach().cpu().numpy()

    # append the model predictions
    total_preds.append(preds)

  # compute the training loss of the epoch
  avg_loss = total_loss / len(train_dataloader)
  
  # predictions are in the form of (no. of batches, size of batch, no. of classes).
  # reshape the predictions in form of (number of samples, no. of classes)
  total_preds  = np.concatenate(total_preds, axis=0)

  #returns the loss and predictions
  return avg_loss, total_preds

def evaluate():
  
  print(&quot;\nEvaluating...&quot;)
  
  # deactivate dropout layers
  model.eval()

  total_loss, total_accuracy = 0, 0
  
  # empty list to save the model predictions
  total_preds = []

  # iterate over batches
  for step,batch in enumerate(val_dataloader):
    
    # Progress update every 50 batches.
    if step % 50 == 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,}.'.format(step, len(val_dataloader)))

    # push the batch to gpu
    batch = [t.to(device) for t in batch]

    sent_id, mask, labels = batch

    # deactivate autograd
    with torch.no_grad():
      
      # model predictions
      preds = model(sent_id, mask)

      # compute the validation loss between actual and predicted values
      loss = cross_entropy(preds,labels)

      total_loss = total_loss + loss.item()

      preds = preds.detach().cpu().numpy()

      total_preds.append(preds)

  # compute the validation loss of the epoch
  avg_loss = total_loss / len(val_dataloader) 

  # reshape the predictions in form of (number of samples, no. of classes)
  total_preds  = np.concatenate(total_preds, axis=0)

  return avg_loss, total_preds

# set initial loss to infinite
best_valid_loss = float('inf')

# empty lists to store training and validation loss of each epoch
train_losses=[]
valid_losses=[]

#for each epoch
for epoch in range(epochs):
     
    print('\n Epoch {:} / {:}'.format(epoch + 1, epochs))
    
    #train model
    train_loss, _ = train()
    
    #evaluate model
    valid_loss, _ = evaluate()
    
    #save the best model
    if valid_loss &lt; best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'saved_weights.pt')
    
    # append training and validation loss
    train_losses.append(train_loss)
    valid_losses.append(valid_loss)
    
    print(f'\nTraining Loss: {train_loss:.3f}')
    print(f'Validation Loss: {valid_loss:.3f}')

the traceback i receive is:
Epoch 1 / 10
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
&lt;ipython-input-105-c5138ddf6b25&gt; in &lt;module&gt;()
     12 
     13     #train model
---&gt; 14     train_loss, _ = train()
     15 
     16     #evaluate model

5 frames
&lt;ipython-input-103-3236a6e339dd&gt; in train()
     24 
     25     # get model predictions for the current batch
---&gt; 26     preds = model(sent_id, mask)
     27 
     28     # compute the loss between actual and predicted values

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--&gt; 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

&lt;ipython-input-99-9ebdcf410f97&gt; in forward(self, sent_id, mask)
     28       _, cls_hs = self.bert(sent_id, attention_mask=mask)
     29 
---&gt; 30       x = self.fc1(cls_hs)
     31 
     32       x = self.relu(x)

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--&gt; 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/linear.py in forward(self, input)
     92 
     93     def forward(self, input: Tensor) -&gt; Tensor:
---&gt; 94         return F.linear(input, self.weight, self.bias)
     95 
     96     def extra_repr(self) -&gt; str:

/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in linear(input, weight, bias)
   1751     if has_torch_function_variadic(input, weight):
   1752         return handle_torch_function(linear, (input, weight), input, weight, bias=bias)
-&gt; 1753     return torch._C._nn.linear(input, weight, bias)
   1754 
   1755 

TypeError: linear(): argument 'input' (position 1) must be Tensor, not str


I've been working on this repo too.
Motivated by the answer provided on this link. There is a class probably named Bert_Arch that inherits the nn.Module and this class has a overriden method named forward. Inside forward method just add the parameter 'return_dict=False' to the self.bert() method call. Like so:
_, cls_hs = self.bert(sent_id, attention_mask=mask, return_dict=False)

This worked for me.
