사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/orchestra-research/ai-research-skills --skill sentencepiece시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
SentencePiece - Language-Independent Tokenization
Unsupervised tokenizer that works on raw text without language-specific preprocessing.
When to use SentencePiece
Use SentencePiece when:
- Building multilingual models (no language-specific rules)
- Working with CJK languages (Chinese, Japanese, Korean)
- Need reproducible tokenization (deterministic vocabulary)
- Want to train on raw text (no pre-tokenization needed)
- Require lightweight deployment (6MB memory, 50k sentences/sec)
Performance:
- Speed: 50,000 sentences/sec
- Memory: ~6MB for loaded model
- Languages: All (language-independent)
Use alternatives instead:
- HuggingFace Tokenizers: Faster training, more flexibility
- tiktoken: OpenAI models (GPT-3.5/4)
- BERT WordPiece: English-centric tasks
Quick start
Installation
bash
# 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 installTrain model
bash
# 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
python
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"Language-independent design
Whitespace as symbol (▁)
python
text = "Hello world"
pieces = sp.encode(text, out_type=str)
print(pieces) # ['▁Hello', '▁world']
# Decode preserves spaces
decoded = sp.decode_pieces(pieces)
print(decoded) # "Hello world"Key principle: Treat text as raw Unicode, whitespace = ▁ (meta symbol)
Tokenization algorithms
BPE (Byte-Pair Encoding)
python
spm.SentencePieceTrainer.train(
input='data.txt',
model_prefix='bpe_model',
vocab_size=16000,
model_type='bpe'
)Used by: mBART
Unigram (default)
python
spm.SentencePieceTrainer.train(
input='data.txt',
model_prefix='unigram_model',
vocab_size=8000,
model_type='unigram'
)Used by: T5, ALBERT, XLNet
Training configuration
Essential parameters
python
spm.SentencePieceTrainer.train(
input='corpus.txt',
model_prefix='m',
vocab_size=32000,
model_type='unigram',
character_coverage=0.9995, # 1.0 for CJK
user_defined_symbols=['[SEP]', '[CLS]'],
unk_piece='<unk>',
num_threads=16
)Character coverage
| Language Type | Coverage | Rationale |
|---|---|---|
| English | 0.9995 | Most common chars |
| CJK (Chinese) | 1.0 | All characters needed |
| Multilingual | 0.9995 | Balance |
Encoding options
Subword regularization
python
# 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
python
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
python
from transformers import T5Tokenizer
# T5 uses SentencePiece internally
tokenizer = T5Tokenizer.from_pretrained('t5-base')
inputs = tokenizer('translate English to French: Hello', return_tensors='pt')Performance benchmarks
Training speed
| Corpus | BPE (16k) | Unigram (8k) |
|---|---|---|
| 100 MB | 1-2 min | 3-4 min |
| 1 GB | 10-15 min | 30-40 min |
Tokenization speed
- SentencePiece: 50,000 sentences/sec
- HF Tokenizers: 200,000 sentences/sec (4× faster)
Supported models
T5 family: t5-base, t5-large (32k vocab, Unigram) ALBERT: albert-base-v2 (30k vocab, Unigram) XLNet: xlnet-base-cased (32k vocab, Unigram) mBART: facebook/mbart-large-50 (250k vocab, BPE)
References
- [Training Guide](references/training.md) - Detailed options, corpus preparation
- [Algorithms](references/algorithms.md) - BPE vs Unigram, subword regularization
Resources
- GitHub: https://github.com/google/sentencepiece ⭐ 10,000+
- Paper: https://arxiv.org/abs/1808.06226 (EMNLP 2018)
- Version: 0.2.0+
