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from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
model_dir = "./" # ${MODEL_DIR}
# load dataset
dataset = load_dataset("mc4", "bn", split="train", streaming=True)
Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
# Instantiate normalizer
tokenizer.normalizer = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}"), " "),
normalizers.Replace("\u09e4", "\u0964"),
normalizers.Replace("\u09e5", "\u0965"),
normalizers.Replace("\u007c", "\u0964"),
normalizers.Replace("\u09f7", "\u0964"),
normalizers.Replace(Regex(r"(?<=[\u0980-\u09ff]):"), "\u0983"),
normalizers.Lowercase(),
]
)
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=[
"<|endoftext|>",
])
# Save files to disk
tokenizer.save(f"{model_dir}/tokenizer.json")
# f = open("demofile3.txt", "w")
# f.write(next(iter(dataset))['text'])
# f.close()
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