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 17 example script of transformers