train_unigram_tokenizer.py

from tokenizers import Tokenizer
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer
from tokenizers.normalizers import NFKC
from tokenizers.pre_tokenizers import UnicodeScripts, Digits, Sequence
from datasets import load_dataset

mc4 = load_dataset('mc4', 'bn', split='train')

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

tokenizer = Tokenizer(Unigram())
tokenizer.normalizer = NFKC()
tokenizer.pre_tokenizer = Sequence([UnicodeScripts(), Digits(individual_digits=True)]) 
trainer = UnigramTrainer(vocab_size=32000, special_tokens=['<s>', '<pad>', '</s>', '<unk>', '<mask>'])
tokenizer.train_from_iterator(batch_iterator(mc4), trainer=trainer, length=len(mc4))
tokenizer.save('tokenizer-mc4-unigram.json')