typeError：dropout（）：参数＆＃x27; input＆＃x27; （位置1）必须是张量，而不是Str Bert模型

Question

嗨，当我训练我的BERT模型进行情感分析时，我会遇到此错误，在该模型中，我的课程有3个结果，而我的输入数据是文本。

因此，当我训练模型时，我会遇到上述错误。我已经搜索了一些指南，并试图将此参数设置为我的bert模型bert_model = bertmodel.from_pretrataining（model_name，return_dict = false）> 但是我仍然遇到与以前相同的错误。我正在使用“ Bert-Base cated”预验证的模型

# Function for a single training iteration
def train_epoch(model, data_loader, loss_fn, optimizer, device, scheduler, n_examples):
    model = model.train()
    losses = []
    correct_predictions = 0
    
    for d in data_loader:
        input_ids = d["input_ids"].to(device)
        attention_mask = d["attention_mask"].to(device)
        targets = d["targets"].to(device)
        
        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())
        
        # Backward prop
        loss.backward()
        
        # Gradient Descent
        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()
        scheduler.step()
        optimizer.zero_grad()
    
    return correct_predictions.double() / n_examples, np.mean(losses)

%%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