File size: 2,446 Bytes
2b3226f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | """SentencePiece tokenizer preserving BashkirRoBERTa's original token IDs."""
from pathlib import Path
import sentencepiece as spm
from transformers import PreTrainedTokenizer
class BashkirRobertaTokenizer(PreTrainedTokenizer):
vocab_files_names = {"vocab_file": "spm_bashkir_bert_16k.model"}
model_input_names = ["input_ids", "attention_mask"]
def __init__(self, vocab_file, **kwargs):
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(model_file=vocab_file)
defaults = {
"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>",
"pad_token": "<pad>", "cls_token": "[CLS]", "sep_token": "[SEP]",
"mask_token": "[MASK]",
}
for key, value in defaults.items():
kwargs.setdefault(key, value)
super().__init__(**kwargs)
@property
def vocab_size(self):
return self.sp_model.get_piece_size()
def get_vocab(self):
return {self.sp_model.id_to_piece(i): i for i in range(self.vocab_size)}
def _tokenize(self, text):
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
return self.sp_model.id_to_piece(index)
def convert_tokens_to_string(self, tokens):
return self.sp_model.decode(tokens)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
if token_ids_1 is not None:
return [self.bos_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.eos_token_id]
return [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
if already_has_special_tokens:
return super().get_special_tokens_mask(token_ids_0, token_ids_1, True)
if token_ids_1 is None:
return [1] + [0] * len(token_ids_0) + [1]
return [1] + [0] * len(token_ids_0) + [1] + [0] * len(token_ids_1) + [1]
def save_vocabulary(self, save_directory, filename_prefix=None):
source = Path(self.vocab_file)
name = ((filename_prefix + "-") if filename_prefix else "") + self.vocab_files_names["vocab_file"]
target = Path(save_directory) / name
target.write_bytes(source.read_bytes())
return (str(target),)
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