Help understanding how to build a dataset for language modeling as with the old TextDataset

Hello,

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 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 :))


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



Resolved