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'''Training script for tokenizer'''
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
from .utils import model_dir
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# 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=50265,
min_frequency=2,
special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"]
)
# Save files to disk
tokenizer_path = model_dir / 'tokenizer.json'
tokenizer.save(str(tokenizer_path))
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