| |
| import json |
| from collections.abc import Iterator |
| from typing import Union |
|
|
| from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers |
| from tokenizers.implementations.base_tokenizer import BaseTokenizer |
| from tokenizers.models import Unigram |
| from tokenizers.processors import TemplateProcessing |
|
|
|
|
| class SentencePieceUnigramTokenizer(BaseTokenizer): |
| """ |
| This class is a copy of `DeDLOC's tokenizer implementation <https://github.com/yandex-research/DeDLOC/blob/main/sahajbert/tokenizer/tokenizer_model.py>`__ . |
| |
| Custom SentencePiece Unigram Tokenizer with NMT, NKFC, spaces and lower-casing characters normalization |
| Represents the Unigram algorithm, with the pretokenization used by SentencePiece |
| """ |
|
|
| def __init__( |
| self, |
| replacement: str = "▁", |
| add_prefix_space: bool = True, |
| unk_token: Union[str, AddedToken] = "<unk>", |
| eos_token: Union[str, AddedToken] = "</s>", |
| pad_token: Union[str, AddedToken] = "<pad>", |
| ): |
| self.special_tokens = { |
| "pad": {"id": 0, "token": pad_token}, |
| "eos": {"id": 1, "token": eos_token}, |
| "unk": {"id": 2, "token": unk_token}, |
| } |
|
|
| self.special_tokens_list = [None] * len(self.special_tokens) |
| for token_dict in self.special_tokens.values(): |
| self.special_tokens_list[token_dict["id"]] = token_dict["token"] |
|
|
| tokenizer = Tokenizer(Unigram()) |
|
|
| tokenizer.normalizer = normalizers.Sequence( |
| [ |
| normalizers.Nmt(), |
| normalizers.NFKC(), |
| normalizers.Replace(Regex(" {2,}"), " "), |
| normalizers.Lowercase(), |
| ] |
| ) |
| tokenizer.pre_tokenizer = pre_tokenizers.Sequence( |
| [ |
| pre_tokenizers.Metaspace( |
| replacement=replacement, prepend_scheme="always" if add_prefix_space else "never" |
| ), |
| pre_tokenizers.Digits(individual_digits=True), |
| pre_tokenizers.Punctuation(), |
| ] |
| ) |
| tokenizer.decoder = decoders.Metaspace( |
| replacement=replacement, prepend_scheme="always" if add_prefix_space else "never" |
| ) |
|
|
| tokenizer.post_processor = TemplateProcessing( |
| single=f"$A {self.special_tokens['eos']['token']}", |
| special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])], |
| ) |
|
|
| parameters = { |
| "model": "SentencePieceUnigram", |
| "replacement": replacement, |
| "add_prefix_space": add_prefix_space, |
| } |
|
|
| super().__init__(tokenizer, parameters) |
|
|
| def train( |
| self, |
| files: Union[str, list[str]], |
| vocab_size: int = 8000, |
| show_progress: bool = True, |
| ): |
| """Train the model using the given files""" |
|
|
| trainer = trainers.UnigramTrainer( |
| vocab_size=vocab_size, |
| special_tokens=self.special_tokens_list, |
| show_progress=show_progress, |
| ) |
|
|
| if isinstance(files, str): |
| files = [files] |
| self._tokenizer.train(files, trainer=trainer) |
|
|
| self.add_unk_id() |
|
|
| def train_from_iterator( |
| self, |
| iterator: Union[Iterator[str], Iterator[Iterator[str]]], |
| vocab_size: int = 8000, |
| show_progress: bool = True, |
| ): |
| """Train the model using the given iterator""" |
|
|
| trainer = trainers.UnigramTrainer( |
| vocab_size=vocab_size, |
| special_tokens=self.special_tokens_list, |
| show_progress=show_progress, |
| ) |
|
|
| self._tokenizer.train_from_iterator(iterator, trainer=trainer) |
|
|
| self.add_unk_id() |
|
|
| def add_unk_id(self): |
| tokenizer_json = json.loads(self._tokenizer.to_str()) |
|
|
| tokenizer_json["model"]["unk_id"] = self.special_tokens["unk"]["id"] |
|
|
| self._tokenizer = Tokenizer.from_str(json.dumps(tokenizer_json)) |
|
|
