train_tokenizer.py

#!/usr/bin/env python3
from datasets import load_dataset

from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
from tokenizers.pre_tokenizers import Whitespace
from tokenizers import normalizers

# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()

dataset = load_dataset("oscar", "unshuffled_deduplicated_de", split="train")


def batch_iterator(batch_size=1000):
    for i in range(0, len(dataset), batch_size):
        yield dataset[i: i + batch_size]["text"]


# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
    "<s>",
    "<pad>",
    "</s>",
    "<unk>",
    "<mask>",
])

# Save files to disk
tokenizer.save("./roberta-base-german/tokenizer.json")