[Question]: ernie1.0不报错但是3.0报index out of range in self

[mnbvcxzz1375]

请提出你的问题

打乱数据

data = data.sample(frac=1, random_state=42).reset_index(drop=True)

分割数据集为训练集和测试集

train_data, test_data = train_test_split(data, test_size=0.3, random_state=42)

加载预训练的 ERNIE 模型和分词器

tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-3.0-mini-zh")
ernie_model = AutoModel.from_pretrained("nghuyong/ernie-3.0-mini-zh")

假设 train_data 和 test_data 是已经加载的数据框，包含列 '0' 和 'label'

train_texts = train_data['0'].tolist()
train_labels = train_data['label'].tolist()

test_texts = test_data['0'].tolist()
test_labels = test_data['label'].tolist()

处理编码和截断

def preprocess_data(texts, tokenizer, max_length=128):
encodings = tokenizer(texts, max_length=max_length, padding='max_length', truncation=True)
return torch.tensor(encodings['input_ids'])

train_inputs = preprocess_data(train_texts, tokenizer)
test_inputs = preprocess_data(test_texts, tokenizer)

train_labels = torch.tensor(train_labels)
test_labels = torch.tensor(test_labels)

---

IndexError Traceback (most recent call last)
Cell In[33], line 97
95 for inputs, labels in train_loader:
96 optimizer.zero_grad()
---> 97 outputs = model(inputs)
98 loss = criterion(outputs, labels.unsqueeze(1))
99 loss.backward()

File ~.conda\envs\deeplearning\lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)

File ~.conda\envs\deeplearning\lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
1536 # If we don't have any hooks, we want to skip the rest of the logic in
1537 # this function, and just call forward.
1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1543 try:
1544 result = None

Cell In[33], line 54, in TextClassifier.forward(self, x)
53 def forward(self, x):
---> 54 x = self.embedding(x)
55 x = x.permute(0, 2, 1)
56 x = self.conv1d_1(x)

File ~.conda\envs\deeplearning\lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)

File ~.conda\envs\deeplearning\lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
1536 # If we don't have any hooks, we want to skip the rest of the logic in
1537 # this function, and just call forward.
1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1543 try:
1544 result = None

File ~.conda\envs\deeplearning\lib\site-packages\torch\nn\modules\sparse.py:163, in Embedding.forward(self, input)
162 def forward(self, input: Tensor) -> Tensor:
--> 163 return F.embedding(
164 input, self.weight, self.padding_idx, self.max_norm,
165 self.norm_type, self.scale_grad_by_freq, self.sparse)

File ~.conda\envs\deeplearning\lib\site-packages\torch\nn\functional.py:2264, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
2258 # Note [embedding_renorm set_grad_enabled]
2259 # XXX: equivalent to
2260 # with torch.no_grad():
2261 # torch.embedding_renorm_
2262 # remove once script supports set_grad_enabled
2263 no_grad_embedding_renorm(weight, input, max_norm, norm_type)
-> 2264 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)

IndexError: index out of range in self