我正在学习如何使用Huggingface Transformers库，在Kaggle的Twitter灾难数据集上构建一个二元分类BERT模型。
进入训练循环后，在执行forward()函数期间，我遇到了以下错误：
Epoch 1/50
----------
Aici incepe train_epoch
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
cpuset_checked))
----Checkpoint train_epoch 2----
----Checkpoint train_epoch 2----
----forward checkpoint 1----
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-175-fd9f98819b6f> in <module>()
23 device,
24 scheduler,
---> 25 df_train.shape[0]
26 )
27 print(f'Train loss {train_loss} Accuracy:{train_acc}')
4 frames
<ipython-input-173-bfbecd87c5ec> in train_epoch(model, data_loader, loss_fn, optimizer, device, scheduler, n_examples)
21 targets = d['targets'].to(device)
22 print('----Checkpoint train_epoch 2----')
---> 23 outputs = model(input_ids=input_ids,attention_mask=attention_mask)
24 print('----Checkpoint train_epoch 3----')
25 _,preds = torch.max(outputs,dim=1)
/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:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
<ipython-input-171-e754ea3edc36> in forward(self, input_ids, attention_mask)
16 input_ids=input_ids,
17 attention_mask=attention_mask,
---> 18 return_dict=False)
19
20 print('----forward checkpoint 2-----')
/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:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/usr/local/lib/python3.7/dist-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)
923 elif input_ids is not None:
924 input_shape = input_ids.size()
--> 925 batch_size, seq_length = input_shape
926 elif inputs_embeds is not None:
927 input_shape = inputs_embeds.size()[:-1]
ValueError: too many values to unpack (expected 2)
起初，我认为它与添加的return_dict=False有关，但我错了。下面是分类器和训练循环的代码
分类器:
class DisasterClassifier(nn.Module):
def __init__(self, n_classes):
super(DisasterClassifier,self).__init__()
self.bert=BertModel.from_pretrained(PRE_TRAINED_MODEL,return_dict=False)
self.drop=nn.Dropout(p=0.3) # in timpul antrenarii, valori aleatorii sunt inlocuite cu 0, cu probabilitate p -> regularization and preventing the co-adaptation of neurons
self.out = nn.Linear(self.bert.config.hidden_size,n_classes)
def forward(self,input_ids,attention_mask):
print('----forward checkpoint 1----')
bertOutput = self.bert(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=False)
print('----forward checkpoint 2-----')
output = self.drop(bertOutput['pooler_output'])
return self.out(output)`
训练周期：
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)
loss_fn = nn.CrossEntropyLoss().to(device)
def train_epoch(model,data_loader,loss_fn,optimizer,device,scheduler,n_examples):
print('Aici incepe train_epoch')
model = model.train()
losses =[]
correct_predictions = 0
for d in data_loader:
print('----Checkpoint train_epoch 2----')
input_ids = d['input_ids'].to(device)
attention_mask=d['attention_mask'].to(device)
targets = d['targets'].to(device)
print('----Checkpoint train_epoch 2----')
outputs = model(input_ids=input_ids,attention_mask=attention_mask)
print('----Checkpoint train_epoch 3----')
_,preds = torch.max(outputs,dim=1)
loss = loss_fn(outputs, targets)
correct_predictions += torch.sum(preds == targets)
losses.append(loss.item())
#backpropagation steps
loss.backward()
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)
还有训练循环：
history = defaultdict(list)
best_accuracy = 0
for epoch in range(EPOCHS):
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))
train_acc, train_loss = train_epoch(
model,
train_data_loader,
loss_fn,
optimizer,
device,
scheduler,
df_train.shape[0]
)
print(f'Train loss {train_loss} Accuracy:{train_acc}')
val_acc, val_loss = eval_model(model,val_data_loader,loss_fn,device,len(df_val))
print(f'Validation 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 val_acc > best_accuracy:
torch.save(model.state_dict(), 'best_model_state.bin')
best_accuracy = val_acc
有人遇到过类似的情况吗？
BertTokenizer时，我遇到了相同的错误。我执行encoding = tokenizer([[prompt, prompt, prompt], [choice0, choice1, choice2]], return_tensors='tf', padding=True))并得到ValueError：too many values to unpack (expected 2)。当我执行encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='tf', padding=True)时它可以工作。有什么想法吗？我想微调TFBertForMultipleChoice，使每个问题（prompt）有三个选择而不是两个：https://huggingface.co/transformers/model_doc/bert.html?highlight=formultiplechoice#tfbertformultiplechoice。 - ayalaall