33 lines
819 B
Python
33 lines
819 B
Python
from datasets import load_dataset
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from tokenizers import ByteLevelBPETokenizer
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from pythainlp.tokenize import word_tokenize
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# load dataset
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dataset = load_dataset("oscar", "unshuffled_deduplicated_th", split="train")
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# Instantiate tokenizer
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tokenizer = ByteLevelBPETokenizer()
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def th_tokenize(text):
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result = " ".join(word_tokenize(text, engine="newmm", keep_whitespace=False))
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return result
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def batch_iterator(batch_size=1000):
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for i in range(0, len(dataset), batch_size):
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yield [th_tokenize(text) for text in dataset[i : i + batch_size]["text"]]
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# Customized training
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tokenizer.train_from_iterator(
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batch_iterator(),
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vocab_size=50265,
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min_frequency=2,
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special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>",],
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)
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# Save files to disk
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tokenizer.save(f"./tokenizer.json")
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