sentencepiece

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npx skills add firecrawl/ai-research-skills --skill sentencepiece

Documentation

SKILL.md

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Documentation

SKILL.md

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.

npx skills add firecrawl/ai-research-skills --skill sentencepiece

Documentation

SKILL.mdView source

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+

npx skills add firecrawl/ai-research-skills --skill sentencepiece

Documentation

SKILL.md

npx skills add firecrawl/ai-research-skills --skill sentencepiece

Documentation

SKILL.mdView source

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+