I am trying to load a custom dataset that I will then use for language modeling. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens limit of most tokenizers.
I would like to understand what is the process to build a text dataset that tokenizes each line, having previously split the documents in the dataset into lines of a “tokenizable” size, as the old TextDataset class would do, where you only had to do the following, and a tokenized dataset without text loss would be available to pass to a DataCollator:
model_checkpoint = 'distilbert-base-uncased' from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) from transformers import TextDataset dataset = TextDataset( tokenizer=tokenizer, file_path="path/to/text_file.txt", block_size=512, )
For now, what I have is the following, which, of course, throws an error/warning because each line is longer than the maximum block size in the tokenizer:
import datasets dataset = datasets.load_dataset('path/to/text_file.txt') model_checkpoint = 'distilbert-base-uncased' tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) def tokenize_function(examples): return tokenizer(examples["text"]) tokenized_datasets = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"]) tokenized_datasets
So what would be the “standard” way of creating a dataset in the way it was done before?
Thank you very much for the help :))