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from torch.utils.data import Dataset
from transformers import AutoTokenizer
import torch
class TokenizedDataset(Dataset):
def __init__(self, custom_dataset, tokenizer, max_seq_len):
"""
custom_dataset: An instance of CustomDataset
tokenizer: An instance of the tokenizer
max_seq_len: Maximum sequence length for padding
"""
self.dataset = custom_dataset
self.tokenizer = tokenizer
self.max_seq_len = max_seq_len
def __len__(self):
# The length is inherited from the custom dataset
return len(self.dataset)
def tokenize_and_pad(self, text_list):
"""
Tokenize and pad a list of text strings.
"""
# Tokenize all text strings in the list
tokens = self.tokenizer(text_list, padding='max_length', max_length=self.max_seq_len, truncation=True, return_tensors="pt")
return tokens
def __getitem__(self, idx):
# Fetch the transformed data from the CustomDataset instance
transformed_data = self.dataset[idx]
# Initialize containers for inputs and optionally labels
tokenized_inputs = {}
tokenized_labels = {}
# Dynamically process each item in the dataset
for key, value in transformed_data.items():
if type(value) == int: # Check if value is an integer
# Convert integer to tensor and directly assign to inputs or labels based on key prefix
if key.startswith('label'):
tokenized_labels[key] = torch.tensor(value) # Convert int to tensor for labels
else:
tokenized_inputs[key] = torch.tensor(value) # Convert int to tensor for inputs
if type(value) == str:
tokenized_data = self.tokenize_and_pad(value)
if key.startswith('label'):
tokenized_labels[key] = tokenized_data['input_ids']
tokenized_labels['attention_mask_' + key] = tokenized_data['attention_mask']
else:
tokenized_inputs[key] = tokenized_data['input_ids']
tokenized_inputs['attention_mask_' + key] = tokenized_data['attention_mask']
# Prepare the return structure, conditionally including 'label' if labels are present
output = {"inputs": tokenized_inputs}
if tokenized_labels: # Check if there are any labels before adding to the output
output["label"] = tokenized_labels
return output
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