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import json |
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from typing import Iterator, List, Union |
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from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers |
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from tokenizers.implementations.base_tokenizer import BaseTokenizer |
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from tokenizers.models import Unigram |
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from tokenizers.processors import TemplateProcessing |
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class SentencePieceUnigramTokenizer(BaseTokenizer): |
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""" |
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This class is a copy of `DeDLOC's tokenizer implementation <https://github.com/yandex-research/DeDLOC/blob/main/sahajbert/tokenizer/tokenizer_model.py>`__ . |
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Custom SentencePiece Unigram Tokenizer with NMT, NKFC, spaces and lower-casing characters normalization |
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Represents the Unigram algorithm, with the pretokenization used by SentencePiece |
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""" |
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def __init__( |
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self, |
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replacement: str = "▁", |
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add_prefix_space: bool = True, |
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unk_token: Union[str, AddedToken] = "<unk>", |
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eos_token: Union[str, AddedToken] = "</s>", |
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pad_token: Union[str, AddedToken] = "<pad>", |
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): |
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self.special_tokens = { |
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"pad": {"id": 0, "token": pad_token}, |
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"eos": {"id": 1, "token": eos_token}, |
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"unk": {"id": 2, "token": unk_token}, |
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} |
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self.special_tokens_list = [None] * len(self.special_tokens) |
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for token_dict in self.special_tokens.values(): |
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self.special_tokens_list[token_dict["id"]] = token_dict["token"] |
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tokenizer = Tokenizer(Unigram()) |
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tokenizer.normalizer = normalizers.Sequence( |
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[ |
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normalizers.Nmt(), |
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normalizers.NFKC(), |
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normalizers.Replace(Regex(" {2,}"), " "), |
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] |
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) |
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tokenizer.pre_tokenizer = pre_tokenizers.Sequence( |
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[ |
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pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space), |
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pre_tokenizers.Digits(individual_digits=True), |
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pre_tokenizers.Punctuation(), |
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] |
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) |
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tokenizer.decoder = decoders.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space) |
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tokenizer.post_processor = TemplateProcessing( |
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single=f"$A {self.special_tokens['eos']['token']}", |
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special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])], |
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) |
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parameters = { |
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"model": "SentencePieceUnigram", |
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"replacement": replacement, |
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"add_prefix_space": add_prefix_space, |
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} |
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super().__init__(tokenizer, parameters) |
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def train( |
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self, |
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files: Union[str, List[str]], |
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vocab_size: int = 8000, |
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show_progress: bool = True, |
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): |
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"""Train the model using the given files""" |
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trainer = trainers.UnigramTrainer( |
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vocab_size=vocab_size, |
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special_tokens=self.special_tokens_list, |
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show_progress=show_progress, |
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) |
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if isinstance(files, str): |
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files = [files] |
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self._tokenizer.train(files, trainer=trainer) |
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self.add_unk_id() |
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def train_from_iterator( |
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self, |
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iterator: Union[Iterator[str], Iterator[Iterator[str]]], |
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vocab_size: int = 8000, |
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show_progress: bool = True, |
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): |
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"""Train the model using the given iterator""" |
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trainer = trainers.UnigramTrainer( |
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vocab_size=vocab_size, |
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special_tokens=self.special_tokens_list, |
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show_progress=show_progress, |
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) |
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self._tokenizer.train_from_iterator(iterator, trainer=trainer) |
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self.add_unk_id() |
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def add_unk_id(self): |
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tokenizer_json = json.loads(self._tokenizer.to_str()) |
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tokenizer_json["model"]["unk_id"] = self.special_tokens["unk"]["id"] |
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self._tokenizer = Tokenizer.from_str(json.dumps(tokenizer_json)) |
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