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input_file = "/content/bothcan.txt" # Replace with actual input file path
model_prefix = "botchan" # Replace with desired model save path
import sentencepiece as spm
spm.SentencePieceTrainer.train(
input=input_file,
model_prefix=model_prefix,
vocab_size=1000, # Adjust as needed, this is just an example value
model_type="unigram", # You can use different models like unigram or bpe
)
from sentencepiece import SentencePieceProcessor
model_path = "botchan.model" # Replace with the actual path
sp_model = SentencePieceProcessor(model_file=model_path)
vocab_size = 4000
import os
from logging import getLogger
from typing import List
from sentencepiece import SentencePieceProcessor
logger = getLogger()
class Tokenizer:
def __init__(self, model_path: str):
# reload tokenizer
assert os.path.isfile(model_path), model_path
self.sp_model = SentencePieceProcessor(model_file=model_path)
logger.info(f"Reloaded SentencePiece model from {model_path}")
# BOS / EOS token IDs
self.n_words: int = self.sp_model.vocab_size()
self.bos_id: int = self.sp_model.bos_id()
self.eos_id: int = self.sp_model.eos_id()
self.pad_id: int = self.sp_model.pad_id()
logger.info(
f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
)
assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
assert type(s) is str
t = self.sp_model.encode(s)
if bos:
t = [self.bos_id] + t
if eos:
t = t + [self.eos_id]
return t
def decode(self, t: List[int]) -> str:
return self.sp_model.decode(t)
tokenizer = Tokenizer(model_path="botchan.model") # Replace with actual model path
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