我正在嘗試使用我的自定義數據集微調 gpt2。我使用擁抱面變壓器的文檔創建了一個基本示例。我收到上述錯誤。我知道這意味著什么：（基本上它是在非標量張量上向后調用）但由于我幾乎只使用 API 調用，所以我不知道如何解決這個問題。有什么建議么？from pathlib import Pathfrom absl import flags, appimport IPythonimport torchfrom transformers import GPT2LMHeadModel, Trainer,  TrainingArgumentsfrom data_reader import GetDataAsPython# this is my custom data, but i get the same error for the basic case below# data = GetDataAsPython('data.json')# data = [data_point.GetText2Text() for data_point in data]# print("Number of data samples is", len(data))data = ["this is a trial text", "this is another trial text"]train_texts = datafrom transformers import GPT2Tokenizertokenizer = GPT2Tokenizer.from_pretrained('gpt2')special_tokens_dict = {'pad_token': '<PAD>'}num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)train_encodigs = tokenizer(train_texts, truncation=True, padding=True)class BugFixDataset(torch.utils.data.Dataset):    def __init__(self, encodings):        self.encodings = encodings        def __getitem__(self, index):        item = {key: torch.tensor(val[index]) for key, val in self.encodings.items()}        return item    def __len__(self):        return len(self.encodings['input_ids'])train_dataset = BugFixDataset(train_encodigs)training_args = TrainingArguments(    output_dir='./results',              num_train_epochs=3,                  per_device_train_batch_size=1,      per_device_eval_batch_size=1,       warmup_steps=500,                    weight_decay=0.01,                   logging_dir='./logs',    logging_steps=10,)model = GPT2LMHeadModel.from_pretrained('gpt2', return_dict=True)model.resize_token_embeddings(len(tokenizer))trainer = Trainer(    model=model,    args=training_args,    train_dataset=train_dataset,)trainer.train()