sentencepiece

Files (3)

• SKILL.md
• algorithms.md · references/
• training.md · references/

SKILL.md (first lines)

# 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 install
```

### Train 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'
)

From Orchestra-Research/AI-Research-SKILLs, MIT.

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