How to build a dataset for language modeling with the datasets library as with the old TextDataset from the transformers library

Question

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,
)

Instead of this way, which is to be deprecated soon, I would like to use the datasets library. For now, what I have is the following, which, of course, throws an error 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"])

So what would be the "standard" way of creating a dataset in the way it was done before but with the datasets lib?

Thank you very much for the help :))

Answer 1

I received an answer for this question on the HuggingFace Datasets forum by @lhoestq

Hi !

If you want to tokenize line by line, you can use this:

max_seq_length = 512
num_proc = 4

def tokenize_function(examples):
    # Remove empty lines
    examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
    return tokenizer(
        examples["text"],
        truncation=True,
        max_length=max_seq_length,
    )

tokenized_dataset = dataset.map(
    tokenize_function,
    batched=True,
    num_proc=num_proc,
    remove_columns=["text"],
)

Though the TextDataset was doing a different processing by concatenating all the texts and building blocks of size 512. If you need this behavior, then you must apply an additional map function after the tokenization:

# Main data processing function that will concatenate all texts from
# our dataset and generate chunks of max_seq_length.
def group_texts(examples):
    # Concatenate all texts.
    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
    total_length = len(concatenated_examples[list(examples.keys())[0]])
    # We drop the small remainder, we could add padding if the model supported it instead of this drop,
    # you can customize this part to your needs.
    total_length = (total_length // max_seq_length) * max_seq_length
    # Split by chunks of max_len.
    result = {
        k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
        for k, t in concatenated_examples.items()
    }
    return result

# Note that with `batched=True`, this map processes 1,000 texts together,
# so group_texts throws away a remainder for each of those groups of 1,000 texts.
# You can adjust that batch_size here but a higher value might be slower to preprocess.

tokenized_dataset = tokenized_dataset.map(
    group_texts,
    batched=True,
    num_proc=num_proc,
)

This code comes from the processing of the run_mlm.py example script of transformers