I was trying to run some epochs to train my sentiment analysis model, at the very last passage, the epochs stopped with the error in the title. I attach the codes here:

Sentiment classifier:

# Build the Sentiment Classifier class 
class SentimentClassifier(nn.Module):
    
    # Constructor class 
    def __init__(self, n_classes):
        super(SentimentClassifier, self).__init__()
        self.bert = BertModel.from_pretrained(MODEL_NAME)
        self.drop = nn.Dropout(p=0.3)
        self.out = nn.Linear(self.bert.config.hidden_size, n_classes)
    
    # Forward propagaion class
    def forward(self, input_ids, attention_mask):
        _, pooled_output = self.bert(
          input_ids=input_ids,
          attention_mask=attention_mask
        )
        #  Add a dropout layer 
        output = self.drop(bertOutput['pooler_output'])
        return self.out(output)

Iterations:

# Number of iterations 
EPOCHS = 10

# Optimizer Adam 
optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)

total_steps = len(train_data_loader) * EPOCHS

scheduler = get_linear_schedule_with_warmup(
    optimizer,
    num_warmup_steps=0,
    num_training_steps=total_steps
)

# Set the loss function 
loss_fn = nn.CrossEntropyLoss().to(device)

evaluation (where the issue raises):

def eval_model(model, data_loader, loss_fn, device, n_examples):
    model = model.eval()
    
    losses = []
    correct_predictions = 0
    
    with torch.no_grad():
        for d in data_loader:
            input_ids = d["input_ids"].to(device)
            attention_mask = d["attention_mask"].to(device)
            targets = d["targets"].to(device)
            
            # Get model ouptuts
            outputs = model(
                input_ids=input_ids,
                attention_mask=attention_mask
            )
            
            _, preds = torch.max(outputs, dim=1)
            loss = loss_fn(outputs, targets)
            
            correct_predictions += torch.sum(preds == targets)
            losses.append(loss.item())
            
    return correct_predictions.double() / n_examples, np.mean(losses)

and finally, the error itself:

%%time

history = defaultdict(list)
best_accuracy = 0

for epoch in range(EPOCHS):
    
    # Show details 
    print(f"Epoch {epoch + 1}/{EPOCHS}")
    print("-" * 10)
    
    train_acc, train_loss = train_epoch(
        model,
        train_data_loader,
        loss_fn,
        optimizer,
        device,
        scheduler,
        len(df_train)
    )
    
    print(f"Train loss {train_loss} accuracy {train_acc}")
    
    # Get model performance (accuracy and loss)
    val_acc, val_loss = eval_model(
        model,
        val_data_loader,
        loss_fn,
        device,
        len(df_val)
    )
    
    print(f"Val   loss {val_loss} accuracy {val_acc}")
    print()
    
    history['train_acc'].append(train_acc)
    history['train_loss'].append(train_loss)
    history['val_acc'].append(val_acc)
    history['val_loss'].append(val_loss)
    
    # If we beat prev performance
    if val_acc > best_accuracy:
        torch.save(model.state_dict(), 'best_model_state.bin')
        best_accuracy = val_acc

Epoch 1/10
----------
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<timed exec> in <module>

<ipython-input-95-6ade5aeecbf2> in train_epoch(model, data_loader, loss_fn, optimizer, device, scheduler, n_examples)
     10         targets = d["targets"].to(device)
     11 
---> 12         outputs = model(
     13             input_ids=input_ids,
     14             attention_mask=attention_mask

6 frames
/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py in dropout(input, p, training, inplace)
   1423         raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
   1424     return (
-> 1425         _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)
   1426     )
   1427 

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

I treid referting transformers with

pip install transformers==3

but nothing.

Any help is appreciated!