|
|
|
import json |
|
from typing import Iterator, List, 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, add_prefix_space=add_prefix_space), |
|
pre_tokenizers.Digits(individual_digits=True), |
|
pre_tokenizers.Punctuation(), |
|
] |
|
) |
|
tokenizer.decoder = decoders.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space) |
|
|
|
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)) |
|
|
