sentencepiece

Chopping text into pieces normally starts by splitting on spaces, which falls apart for Chinese, Japanese and Korean, where there are none; this works straight off the raw characters instead, so one method covers every language.

WHEN TO USE

SCOPE

USE FOR

Train a reproducible, deterministic vocabulary from raw text with no pre-tokenization step.
Tokenize Chinese, Japanese, or Korean text without word-segmentation rules.
Match the exact tokenizer used by T5, ALBERT, XLNet, or mBART.
Add data augmentation by sampling different valid tokenizations of the same sentence.

NOT FOR

Use HuggingFace Tokenizers instead for faster training and more flexibility.
Use tiktoken instead for OpenAI's GPT-3.5 or GPT-4 models.
Use BERT WordPiece instead for English-centric tasks.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (12)

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

# 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"

Language-independent design

Whitespace as symbol (▁)

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)

spm.SentencePieceTrainer.train(
    input='data.txt',
    model_prefix='bpe_model',
    vocab_size=16000,
    model_type='bpe'
)

Used by: mBART

Unigram (default)

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

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

# 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')

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 - Detailed options, corpus preparation
Algorithms - 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+

This skill ships inside the AI Research Skills plugin.

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