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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, XLNe
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentencepiece -a cursor -y
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.
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.
Unsupervised tokenizer that works on raw text without language-specific preprocessing.
Use SentencePiece when:
Performance:
Use alternatives instead:
# 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
# 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
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"
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)
spm.SentencePieceTrainer.train(
input='data.txt',
model_prefix='bpe_model',
vocab_size=16000,
model_type='bpe'
)
Used by: mBART
spm.SentencePieceTrainer.train(
input='data.txt',
model_prefix='unigram_model',
vocab_size=8000,
model_type='unigram'
)
Used by: T5, ALBERT, XLNet
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
)
| Language Type | Coverage | Rationale |
|---|---|---|
| English | 0.9995 | Most common chars |
| CJK (Chinese) | 1.0 | All characters needed |
| Multilingual | 0.9995 | Balance |
# 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.
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
)
from transformers import T5Tokenizer
# T5 uses SentencePiece internally
tokenizer = T5Tokenizer.from_pretrained('t5-base')
inputs = tokenizer('translate English to French: Hello', return_tensors='pt')
| Corpus | BPE (16k) | Unigram (8k) |
|---|---|---|
| 100 MB | 1-2 min | 3-4 min |
| 1 GB | 10-15 min | 30-40 min |
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)