为什么使用huggingface预训练文本生成模型做微调时报错vars() argument must have __dict__ attribute？

class MyIterableDataset(IterableDataset):

    def __init__(self, file_path):
        self.file_path = file_path

    def __iter__(self):
        with open(self.file_path, 'r', encoding="utf-8") as file_obj:
            for line in file_obj:
                line_data = line.strip('\n').split(',')
                yield line_data

dataset = MyIterableDataset('text.csv')

training_args = TrainingArguments(
 output_dir='./results',          # output directory
 num_train_epochs=3,              # total # of training epochs
 per_device_train_batch_size=16,  # batch size per device during training
 per_device_eval_batch_size=64,   # batch size for evaluation
 warmup_steps=500,                # number of warmup steps for learning rate scheduler
 weight_decay=0.01,               # strength of weight decay
 logging_dir='./logs',            # directory for storing logs
 max_steps=100
)

trainer = Trainer(
 model=model,                         # the instantiated 🤗 Transformers model to be trained
 args=training_args,                  # training arguments, defined above
 train_dataset=dataset        # training dataset
)

trainer.train()

用以上方法试图构建数据集，其中.txt文件里面有两列，一列是序号，另一列是文本句子。因为文本生成实在没有label。
然后报错：

查了一些问答结果都有一点抽象，torch基础不好不太理解，想请教我现在的代码应该怎么改