train_tokenizer.py

from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer

# load dataset
# dataset = load_dataset("mc4", "sw", split="train")
dataset = load_dataset("text", "sw", split="train", data_files={"train": ["/home/shared/clean_swahili/train_v1.4.txt"]})

# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()

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=25165, min_frequency=2, special_tokens=[
    "<s>",
    "<pad>",
    "</s>",
    "<unk>",
    "<mask>",
])

# Save files to disk
tokenizer.save("tokenizer.json")