--- name: sentencepiece-47b1d10f419a description: 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. version: 1.0.0 author: Orchestra Research license: MIT tags: [Tokenization, SentencePiece, Language-Independent, BPE, Unigram, Multilingual, CJK Languages, Unicode, Deterministic, Google] dependencies: [sentencepiece, transformers] --- # 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' ) ``` **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='', 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'' 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+ --- Source attribution: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/templates-davila7/cli-tool/components/skills/ai-research/tokenization-sentencepiece/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License (with OpenAI/Anthropic Rider) Copyright (c) 2026 Jeffrey Emanuel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: ADDITIONAL RIDER / RESTRICTION (OpenAI / Anthropic): This rider is part of the "conditions" of this License. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/templates-davila7/cli-tool/components/skills/ai-research/tokenization-sentencepiece/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Data Engineer, Data Scientist, ML / AI Engineer Specialties: API, Cross Platform Tools: Sentencepiece, Transformers Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: b5a1f0f884700232faea6e7a0632de1f6822d7367d34709f3fb12ffcad8b6ea0