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32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import torch
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class SPieceTokenizer:
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@staticmethod
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def from_pretrained(path, **kwargs):
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return SPieceTokenizer(path, **kwargs)
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def __init__(self, tokenizer_path, add_bos=False, add_eos=True):
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self.add_bos = add_bos
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self.add_eos = add_eos
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import sentencepiece
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if torch.is_tensor(tokenizer_path):
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tokenizer_path = tokenizer_path.numpy().tobytes()
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if isinstance(tokenizer_path, bytes):
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self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
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else:
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self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
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def get_vocab(self):
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out = {}
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for i in range(self.tokenizer.get_piece_size()):
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out[self.tokenizer.id_to_piece(i)] = i
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return out
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def __call__(self, string):
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out = self.tokenizer.encode(string)
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return {"input_ids": out}
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def serialize_model(self):
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return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))
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