Unable to create custom dataset and dataloader using torchtext
I have questions regarding building custom dataset and iterator using torchtext. I used the following code found in this post and modified based on my case:
tokenizer = XLNetTokenizer.from_pretrained(&quot;xlnet-base-cased&quot;)
text_field = Field(sequential=True, eos_token=&quot;[CLS]&quot;, tokenize=tokenizer)
label_field = Field(sequential=False, use_vocab=False)
data_fields = [(&quot;file&quot;, None),
               (&quot;text&quot;, text_field),
               (&quot;label&quot;, label_field)]
train, val = train_test_split(input_dt, test_size=0.1)
train.to_csv(&quot;train_output_path&quot;, index=False)
val.to_csv(&quot;val_output_path&quot;, index=False)
train, val = TabularDataset(path=&quot;path&quot;, train=&quot;train.csv&quot;, validation=&quot;val.csv&quot;,
                            format=&quot;csv&quot;, skip_header=True, fields=data_fields)

When it comes to text_field.build_vocab(train), I got this error: TypeError: '&lt;' not supported between instances of 'list' and 'int'.
The only difference between my code and the post is the pre-trained word embeddings. In the post, the author used glove, which I use XLNetTokenizer from transformers package. I also searched for other posts who used the similar method, but they all used the pre-trained word embeddings, therefore they did have such an issue.
Does anyone know how to fix this issue? Many thanks!

I think as you are using a predefined tokenizer you dont't need to build vocab, instead you can follow this steps. Showing an example of how to do it using BERT tokenizer.
Sentences: it is a list of of text data
lables: is the label associated
###tokenizer = XLNetTokenizer.from_pretrained(&quot;xlnet-base-cased&quot;)

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 &amp; 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('{:&gt;5,} training samples'.format(train_size))
print('{:&gt;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.
        )

