from datasets import load_dataset
from tokenizers import ByteLevelBPETokenizer

# Load dataset
kopi = load_dataset("/data/final_train.py", "full",split='train',cache_dir="/data/cache")

datasetv2 = kopi.shuffle(seed=42)
dataset = datasetv2[0:8000000]

# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=100_000):
    for i in range(0, len(dataset), batch_size):
        yield dataset["text"][i: i + batch_size]

# 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("./tokenizer.json")