我在代码中遇到此错误：
movies形状看起来不错，但出现错误model = BertForSequenceClassification.from_pretrained("pretrained/", num_labels=ohe_count)
model.to(device)
from IPython.display import clear_output
train_loss_set = []
train_loss = 0
model.train()
for step, batch in enumerate(train_dataloader):
# добавляем батч для вычисления на GPU
batch = tuple(t.to(device) for t in batch)
# Распаковываем данные из dataloader
b_input_ids, b_input_mask, b_labels = batch
b_input_ids = b_input_ids.type(torch.LongTensor)
b_input_mask = b_input_mask.type(torch.LongTensor)
b_labels = b_labels.type(torch.LongTensor)
b_input_ids = b_input_ids.to(device)
b_input_mask = b_input_mask.to(device)
b_labels = b_labels.to(device)
optimizer.zero_grad()
# Forward pass
loss = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask, labels=b_labels)
train_loss_set.append(loss[0].item())
# Backward pass
loss[0].backward()
optimizer.step()
train_loss += loss[0].item()
clear_output(True)
plt.plot(train_loss_set)
plt.title("Training loss")
plt.xlabel("Batch")
plt.ylabel("Loss")
plt.show()
b_input_ids.shape = torch.Size([32, 100])
b_labels.shape = torch.Size([32, 620])
答案 0 :(得分：0)
没关系，我试图进行多标签分类，但是BertForSequenceClassification无法做到这一点。