Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
HuggingFace Tokenizers: Faster training, more flexibility
tiktoken: OpenAI models (GPT-3.5/4)
BERT WordPiece: English-centric tasks
Quick start
Installation
# Python
pip install sentencepiece
# C++ (requires CMake)
git clone https://github.com/google/sentencepiece.git
cd sentencepiece
mkdir build && cd build
cmake .. && make -j $(nproc)
sudo make install
Train model
# Command-line (BPE with 8000 vocab)
spm_train --input=data.txt --model_prefix=m --vocab_size=8000 --model_type=bpe
# Python API
import sentencepiece as spm
spm.SentencePieceTrainer.train(
input='data.txt',
model_prefix='m',
vocab_size=8000,
model_type='bpe'
)
Training time: ~1-2 minutes for 100MB corpus
Encode and decode
import sentencepiece as spm
# Load model
sp = spm.SentencePieceProcessor(model_file='m.model')
# Encode to pieces
pieces = sp.encode('This is a test', out_type=str)
print(pieces) # ['▁This', '▁is', '▁a', '▁test']
# Encode to IDs
ids = sp.encode('This is a test', out_type=int)
print(ids) # [284, 47, 11, 1243]
# Decode
text = sp.decode(ids)
print(text) # "This is a test"
# Sample different tokenizations
for _ in range(3):
pieces = sp.encode('tokenization', out_type=str, enable_sampling=True, alpha=0.1)
print(pieces)
# Output (different each time):
# ['▁token', 'ization']
# ['▁tok', 'en', 'ization']
Use case: Data augmentation for robustness.
Common patterns
T5-style training
spm.SentencePieceTrainer.train(
input='c4_corpus.txt',
model_prefix='t5',
vocab_size=32000,
model_type='unigram',
user_defined_symbols=[f'<extra_id_{i}>' for i in range(100)],
unk_id=2,
eos_id=1,
pad_id=0
)
Integration with transformers
from transformers import T5Tokenizer
# T5 uses SentencePiece internally
tokenizer = T5Tokenizer.from_pretrained('t5-base')
inputs = tokenizer('translate English to French: Hello', return_tensors='pt')
