深度学习roberta模型出现问题，如何解决

Question

在运行roberta进行文本分类的时候，出现了下面的问题，该如何解决，请大家赐教：

class MLMDataset(torch.utils.data.Dataset):
    def __init__(self, is_train, texts, tokenizer):
        self.is_train = is_train
        self.tokenizer = tokenizer
        if self.is_train:
            self.data = texts
        else:
            self.data = texts
        ### only use portion of data
        length = int(len(self.data)/1)
        self.data = self.data[:length]
        ###

    def __getitem__(self, idx):
        item = self.tokenizer(self.data[idx], padding='max_length', is_split_into_words = False,truncation=True, return_tensors="pt")
        
        item['labels'] = item['input_ids'].clone()
        
        probability_matrix = torch.full(item['labels'].shape, 0.15)
        special_tokens_mask = [self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in item['labels'].tolist()]
        probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
        masked_indices = torch.bernoulli(probability_matrix).bool()
        item['labels'][~masked_indices] = -100

        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
        indices_replaced = torch.bernoulli(torch.full(item['labels'].shape, 0.8)).bool() & masked_indices
        item['input_ids'][indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)

        # 10% of the time, we replace masked input tokens with random word
        indices_random = torch.bernoulli(torch.full(item['labels'].shape, 0.5)).bool() & masked_indices & ~indices_replaced
        random_words = torch.randint(len(self.tokenizer), item['labels'].shape, dtype=torch.long)
        item['input_ids'][indices_random] = random_words[indices_random]

        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
        item['input_ids'] = item['input_ids'][0]
        item['attention_mask'] = item['attention_mask'][0]
        item['labels'] = item['labels'][0]
        return item

    def __len__(self):
        return len(self.data)

datagram

运行结果及报错内容

ValueError: text input must of type str (single example), List[str] (batch or single pretokenized example) or List[List[str]] (batch of pretokenized examples).

Answer 1

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