nb-t5-base / t5_tokenizer_model.py
#!/usr/bin/env python3
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))