RuntimeError，在 IA tryna 上工作使用预训练的 BERT 模型

Question

Hi here is a part of my code to use a pre-trained bert model for classification:

    model = BertForSequenceClassification.from_pretrained(
     "bert-base-uncased", # Use the 12-layer BERT model, with an uncased vocab.
     num_labels = 2, # The number of output labels--2 for binary classification.
                    # You can increase this for multi-class tasks.   
     output_attentions = False, # Whether the model returns attentions weights.
     output_hidden_states = False, # Whether the model returns all hidden-states.
    )

...

for step, batch in enumerate(train_dataloader):

b_input_ids = batch[0].to(device)
    b_input_mask = batch[1].to(device)
    b_labels = batch[2].to(device)

outputs = model(b_input_ids, 
                token_type_ids=None,
                attention_mask=b_input_mask, 
                labels=b_labels)

but then I receive this error message:

RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long;

but got torch.IntTensor instead (while checking arguments for embedding) So I think I should transform my b_input_ids to tensor but don't know how to do it. Thanks a lot in advance for your help everyone !

Answer 1

最后成功使用 .to(torch.int64)