[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADZKGTAW2S1YPP1YCHRN01":3,"skill-same:01M6ADZKGTAW2S1YPP1YCHRN01":91},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6ADZKGTAW2S1YPP1YCHRN01","tokenization-sentencepiece",null,"Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences\u002Fsec), 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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"sentencepiece","tokenization sentencepiece","Tokenization","algorithms","when","you","Unicode","CJK Languages","Language-Independent","Unigram","BPE","cjk","as","google","deterministic","multilingual","text","Transformers","Reproducible","fast","support","research","memory","2026-11-10 10:12:30",false,"---\nname: tokenization-sentencepiece\ndescription: Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences\u002Fsec), 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.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Tokenization, SentencePiece, Language-Independent, BPE, Unigram, Multilingual, CJK Languages, Unicode, Deterministic, Google]\n  dependencies: [sentencepiece, transformers]\n---\n\n# SentencePiece - Language-Independent Tokenization\n\nUnsupervised tokenizer that works on raw text without language-specific preprocessing.\n\n## When to use SentencePiece\n\n**Use SentencePiece when:**\n- Building multilingual models (no language-specific rules)\n- Working with CJK languages (Chinese, Japanese, Korean)\n- Need reproducible tokenization (deterministic vocabulary)\n- Want to train on raw text (no pre-tokenization needed)\n- Require lightweight deployment (6MB memory, 50k sentences\u002Fsec)\n\n**Performance**:\n- **Speed**: 50,000 sentences\u002Fsec\n- **Memory**: ~6MB for loaded model\n- **Languages**: All (language-independent)\n\n**Use alternatives instead**:\n- **HuggingFace Tokenizers**: Faster training, more flexibility\n- **tiktoken**: OpenAI models (GPT-3.5\u002F4)\n- **BERT WordPiece**: English-centric tasks\n\n## Quick start\n\n### Installation\n\n```bash\n# Python\npip install sentencepiece\n\n# C++ (requires CMake)\ngit clone https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fsentencepiece.git\ncd sentencepiece\nmkdir build && cd build\ncmake .. && make -j $(nproc)\nsudo make install\n```\n\n### Train model\n\n```bash\n# Command-line (BPE with 8000 vocab)\nspm_train --input=data.txt --model_prefix=m --vocab_size=8000 --model_type=bpe\n\n# Python API\nimport sentencepiece as spm\n\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='m',\n    vocab_size=8000,\n    model_type='bpe'\n)\n```\n\n**Training time**: ~1-2 minutes for 100MB corpus\n\n### Encode and decode\n\n```python\nimport sentencepiece as spm\n\n# Load model\nsp = spm.SentencePieceProcessor(model_file='m.model')\n\n# Encode to pieces\npieces = sp.encode('This is a test', out_type=str)\nprint(pieces)  # ['▁This', '▁is', '▁a', '▁test']\n\n# Encode to IDs\nids = sp.encode('This is a test', out_type=int)\nprint(ids)  # [284, 47, 11, 1243]\n\n# Decode\ntext = sp.decode(ids)\nprint(text)  # \"This is a test\"\n```\n\n## Language-independent design\n\n### Whitespace as symbol (▁)\n\n```python\ntext = \"Hello world\"\npieces = sp.encode(text, out_type=str)\nprint(pieces)  # ['▁Hello', '▁world']\n\n# Decode preserves spaces\ndecoded = sp.decode_pieces(pieces)\nprint(decoded)  # \"Hello world\"\n```\n\n**Key principle**: Treat text as raw Unicode, whitespace = ▁ (meta symbol)\n\n## Tokenization algorithms\n\n### BPE (Byte-Pair Encoding)\n\n```python\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='bpe_model',\n    vocab_size=16000,\n    model_type='bpe'\n)\n```\n\n**Used by**: mBART\n\n### Unigram (default)\n\n```python\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='unigram_model',\n    vocab_size=8000,\n    model_type='unigram'\n)\n```\n\n**Used by**: T5, ALBERT, XLNet\n\n## Training configuration\n\n### Essential parameters\n\n```python\nspm.SentencePieceTrainer.train(\n    input='corpus.txt',\n    model_prefix='m',\n    vocab_size=32000,\n    model_type='unigram',\n    character_coverage=0.9995,  # 1.0 for CJK\n    user_defined_symbols=['[SEP]', '[CLS]'],\n    unk_piece='\u003Cunk>',\n    num_threads=16\n)\n```\n\n### Character coverage\n\n| Language Type | Coverage | Rationale |\n|---------------|----------|-----------|\n| English       | 0.9995   | Most common chars |\n| CJK (Chinese) | 1.0      | All characters needed |\n| Multilingual  | 0.9995   | Balance |\n\n## Encoding options\n\n### Subword regularization\n\n```python\n# Sample different tokenizations\nfor _ in range(3):\n    pieces = sp.encode('tokenization', out_type=str, enable_sampling=True, alpha=0.1)\n    print(pieces)\n\n# Output (different each time):\n# ['▁token', 'ization']\n# ['▁tok', 'en', 'ization']\n```\n\n**Use case**: Data augmentation for robustness.\n\n## Common patterns\n\n### T5-style training\n\n```python\nspm.SentencePieceTrainer.train(\n    input='c4_corpus.txt',\n    model_prefix='t5',\n    vocab_size=32000,\n    model_type='unigram',\n    user_defined_symbols=[f'\u003Cextra_id_{i}>' for i in range(100)],\n    unk_id=2,\n    eos_id=1,\n    pad_id=0\n)\n```\n\n### Integration with transformers\n\n```python\nfrom transformers import T5Tokenizer\n\n# T5 uses SentencePiece internally\ntokenizer = T5Tokenizer.from_pretrained('t5-base')\ninputs = tokenizer('translate English to French: Hello', return_tensors='pt')\n```\n\n## Performance benchmarks\n\n### Training speed\n\n| Corpus | BPE (16k) | Unigram (8k) |\n|--------|-----------|--------------|\n| 100 MB | 1-2 min   | 3-4 min      |\n| 1 GB   | 10-15 min | 30-40 min    |\n\n### Tokenization speed\n\n- **SentencePiece**: 50,000 sentences\u002Fsec\n- **HF Tokenizers**: 200,000 sentences\u002Fsec (4× faster)\n\n## Supported models\n\n**T5 family**: `t5-base`, `t5-large` (32k vocab, Unigram)\n**ALBERT**: `albert-base-v2` (30k vocab, Unigram)\n**XLNet**: `xlnet-base-cased` (32k vocab, Unigram)\n**mBART**: `facebook\u002Fmbart-large-50` (250k vocab, BPE)\n\n## References\n\n- **[Training Guide](references\u002Ftraining.md)** - Detailed options, corpus preparation\n- **[Algorithms](references\u002Falgorithms.md)** - BPE vs Unigram, subword regularization\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fsentencepiece ⭐ 10,000+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.06226 (EMNLP 2018)\n- **Version**: 0.2.0+\n\n\n","# SentencePiece - Language-Independent Tokenization\n\nUnsupervised tokenizer that works on raw text without language-specific preprocessing.\n\n## When to use SentencePiece\n\n**Use SentencePiece when:**\n- Building multilingual models (no language-specific rules)\n- Working with CJK languages (Chinese, Japanese, Korean)\n- Need reproducible tokenization (deterministic vocabulary)\n- Want to train on raw text (no pre-tokenization needed)\n- Require lightweight deployment (6MB memory, 50k sentences\u002Fsec)\n\n**Performance**:\n- **Speed**: 50,000 sentences\u002Fsec\n- **Memory**: ~6MB for loaded model\n- **Languages**: All (language-independent)\n\n**Use alternatives instead**:\n- **HuggingFace Tokenizers**: Faster training, more flexibility\n- **tiktoken**: OpenAI models (GPT-3.5\u002F4)\n- **BERT WordPiece**: English-centric tasks\n\n## Quick start\n\n### Installation\n\n```bash\n# Python\npip install sentencepiece\n\n# C++ (requires CMake)\ngit clone https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fsentencepiece.git\ncd sentencepiece\nmkdir build && cd build\ncmake .. && make -j $(nproc)\nsudo make install\n```\n\n### Train model\n\n```bash\n# Command-line (BPE with 8000 vocab)\nspm_train --input=data.txt --model_prefix=m --vocab_size=8000 --model_type=bpe\n\n# Python API\nimport sentencepiece as spm\n\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='m',\n    vocab_size=8000,\n    model_type='bpe'\n)\n```\n\n**Training time**: ~1-2 minutes for 100MB corpus\n\n### Encode and decode\n\n```python\nimport sentencepiece as spm\n\n# Load model\nsp = spm.SentencePieceProcessor(model_file='m.model')\n\n# Encode to pieces\npieces = sp.encode('This is a test', out_type=str)\nprint(pieces)  # ['▁This', '▁is', '▁a', '▁test']\n\n# Encode to IDs\nids = sp.encode('This is a test', out_type=int)\nprint(ids)  # [284, 47, 11, 1243]\n\n# Decode\ntext = sp.decode(ids)\nprint(text)  # \"This is a test\"\n```\n\n## Language-independent design\n\n### Whitespace as symbol (▁)\n\n```python\ntext = \"Hello world\"\npieces = sp.encode(text, out_type=str)\nprint(pieces)  # ['▁Hello', '▁world']\n\n# Decode preserves spaces\ndecoded = sp.decode_pieces(pieces)\nprint(decoded)  # \"Hello world\"\n```\n\n**Key principle**: Treat text as raw Unicode, whitespace = ▁ (meta symbol)\n\n## Tokenization algorithms\n\n### BPE (Byte-Pair Encoding)\n\n```python\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='bpe_model',\n    vocab_size=16000,\n    model_type='bpe'\n)\n```\n\n**Used by**: mBART\n\n### Unigram (default)\n\n```python\nspm.SentencePieceTrainer.train(\n    input='data.txt',\n    model_prefix='unigram_model',\n    vocab_size=8000,\n    model_type='unigram'\n)\n```\n\n**Used by**: T5, ALBERT, XLNet\n\n## Training configuration\n\n### Essential parameters\n\n```python\nspm.SentencePieceTrainer.train(\n    input='corpus.txt',\n    model_prefix='m',\n    vocab_size=32000,\n    model_type='unigram',\n    character_coverage=0.9995,  # 1.0 for CJK\n    user_defined_symbols=['[SEP]', '[CLS]'],\n    unk_piece='\u003Cunk>',\n    num_threads=16\n)\n```\n\n### Character coverage\n\n| Language Type | Coverage | Rationale |\n|---------------|----------|-----------|\n| English       | 0.9995   | Most common chars |\n| CJK (Chinese) | 1.0      | All characters needed |\n| Multilingual  | 0.9995   | Balance |\n\n## Encoding options\n\n### Subword regularization\n\n```python\n# Sample different tokenizations\nfor _ in range(3):\n    pieces = sp.encode('tokenization', out_type=str, enable_sampling=True, alpha=0.1)\n    print(pieces)\n\n# Output (different each time):\n# ['▁token', 'ization']\n# ['▁tok', 'en', 'ization']\n```\n\n**Use case**: Data augmentation for robustness.\n\n## Common patterns\n\n### T5-style training\n\n```python\nspm.SentencePieceTrainer.train(\n    input='c4_corpus.txt',\n    model_prefix='t5',\n    vocab_size=32000,\n    model_type='unigram',\n    user_defined_symbols=[f'\u003Cextra_id_{i}>' for i in range(100)],\n    unk_id=2,\n    eos_id=1,\n    pad_id=0\n)\n```\n\n### Integration with transformers\n\n```python\nfrom transformers import T5Tokenizer\n\n# T5 uses SentencePiece internally\ntokenizer = T5Tokenizer.from_pretrained('t5-base')\ninputs = tokenizer('translate English to French: Hello', return_tensors='pt')\n```\n\n## Performance benchmarks\n\n### Training speed\n\n| Corpus | BPE (16k) | Unigram (8k) |\n|--------|-----------|--------------|\n| 100 MB | 1-2 min   | 3-4 min      |\n| 1 GB   | 10-15 min | 30-40 min    |\n\n### Tokenization speed\n\n- **SentencePiece**: 50,000 sentences\u002Fsec\n- **HF Tokenizers**: 200,000 sentences\u002Fsec (4× faster)\n\n## Supported models\n\n**T5 family**: `t5-base`, `t5-large` (32k vocab, Unigram)\n**ALBERT**: `albert-base-v2` (30k vocab, Unigram)\n**XLNet**: `xlnet-base-cased` (32k vocab, Unigram)\n**mBART**: `facebook\u002Fmbart-large-50` (250k vocab, BPE)\n\n## References\n\n- **[Training Guide](references\u002Ftraining.md)** - Detailed options, corpus preparation\n- **[Algorithms](references\u002Falgorithms.md)** - BPE vs Unigram, subword regularization\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fsentencepiece ⭐ 10,000+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.06226 (EMNLP 2018)\n- **Version**: 0.2.0+\n\n\n",{"name":5,"license":15,"metadata":47,"description":7},{"tags":48,"author":53,"version":54,"dependencies":55},[21,49,27,29,28,50,26,25,51,52],"SentencePiece","Multilingual","Deterministic","Google","Orchestra Research","1.0.0",[19,56],"transformers","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add davila7\u002Fclaude-code-templates --skill tokenization-sentencepiece",{"tool":73,"command":74},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill tokenization-sentencepiece",{"tool":76,"command":77},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill tokenization-sentencepiece","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002FSKILL.md",true,[81,87],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Falgorithms.md","REFERENCE","参考文档",4212,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002Freferences\u002Falgorithms.md",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Ftraining.md",6240,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Ftokenization-sentencepiece\u002Freferences\u002Ftraining.md",[92,123,150,183,218,242],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":103,"publishedAt":42},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[98],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[104,105,106,107,108,109,110,111,112,23,113,114,115,116,117,118,119,120,121,122],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":130,"publishedAt":42},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[129],{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},[131,132,133,134,135,136,137,138,139,140,141,23,142,143,144,145,146,147,148,149],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":165,"publishedAt":42},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[156,159,160],{"categoryNo":157,"categoryName":158,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":161,"categoryName":162,"taxonomyType":163,"taxonomyTypeDesc":164},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[166,167,168,169,170,171,172,109,173,174,175,176,177,178,179,112,180,181,23,182],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":187,"licenseFileUrl":188,"sourceHtmlUrl":189,"categories":190,"keywords":194,"publishedAt":42},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). 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