Python 3.x 无法使用torchtext创建自定义数据集和数据加载器

我对使用

torchtext

构建自定义数据集和迭代器有疑问。我使用了本文中的以下代码，并根据我的案例进行了修改：

tokenizer=XLNetTokenizer.from_pretrained（“xlnet base cased”）
text_field=field（sequential=True，eos_token=“[CLS]”，tokenize=tokenizer）
label\u field=field（sequential=False，use\u vocab=False）
数据字段=[（“文件”，无），
（“文本”，文本字段），
（“标签”，标签\字段）]
列车，val=列车试验分离（输入dt，试验尺寸=0.1）
列车至csv（“列车输出路径”，索引=False）
val.to_csv（“val_输出路径”，索引=False）
train，val=tablerdataset（path=“path”，train=“train.csv”，validation=“val.csv”，
format=“csv”，跳过标题=真，字段=数据字段）

---

当涉及到

text\u field.build\u vocab（train）

时，我遇到了一个错误：

TypeError:“我认为当您使用预定义的标记器时，您不需要构建vocab，相反，您可以按照以下步骤操作。显示如何使用BERT标记器执行此操作的示例

句子
：它是一个文本数据列表

标签
：标签是否关联

###tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)

# Tokenize all of the sentences and map the tokens to thier word IDs.

input_ids = []
attention_masks = []

# For every sentence...

for sent in sentences:
    # `encode_plus` will:
    #   (1) Tokenize the sentence.
    #   (2) Prepend the `[CLS]` token to the start.
    #   (3) Append the `[SEP]` token to the end.
    #   (4) Map tokens to their IDs.
    #   (5) Pad or truncate the sentence to `max_length`
    #   (6) Create attention masks for [PAD] tokens.
    encoded_dict = tokenizer.encode_plus(
                        sent,                      # Sentence to encode.
                        add_special_tokens = True, # Add '[CLS]' and '[SEP]'
                        max_length = 100,           # Pad & truncate all sentences.
                        pad_to_max_length = True,
                        return_attention_mask = True,   # Construct attn. masks.
                        return_tensors = 'pt',     # Return pytorch tensors.
                   )
    
    # Add the encoded sentence to the list.    
    input_ids.append(encoded_dict['input_ids'])
    
    # And its attention mask (simply differentiates padding from non-padding).
    attention_masks.append(encoded_dict['attention_mask'])


# Convert the lists into tensors.
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
labels = torch.tensor(labels)

# Print sentence 0, now as a list of IDs.
print('Original: ', sentences[0])
print('Token IDs:', input_ids[0])

### Not combine the input id , mask and labels and divide the dataset

#:
from torch.utils.data import TensorDataset, random_split

# Combine the training inputs into a TensorDataset.
dataset = TensorDataset(input_ids, attention_masks, labels)

# Create a 90-10 train-validation split.

# Calculate the number of samples to include in each set.
train_size = int(0.90 * len(dataset))
val_size = len(dataset) - train_size

# Divide the dataset by randomly selecting samples.
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])

print('{:>5,} training samples'.format(train_size))
print('{:>5,} validation samples'.format(val_size))

### Not you call loader of these datasets


from torch.utils.data import DataLoader, RandomSampler, SequentialSampler

# The DataLoader needs to know our batch size for training, so we specify it 
# here. For fine-tuning BERT on a specific task, the authors recommend a batch 
# size of 16 or 32.
batch_size = 32

# Create the DataLoaders for our training and validation sets.
# We'll take training samples in random order. 
train_dataloader = DataLoader(
            train_dataset,  # The training samples.
            sampler = RandomSampler(train_dataset), # Select batches randomly
            batch_size = batch_size # Trains with this batch size.
        )

# For validation the order doesn't matter, so we'll just read them sequentially.
validation_dataloader = DataLoader(
            val_dataset, # The validation samples.
            sampler = SequentialSampler(val_dataset), # Pull out batches sequentially.
            batch_size = batch_size # Evaluate with this batch size.
        )