[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EX1C2RZFF01C1GWD683MKM":3,"skill-same:01M6EX1C2RZFF01C1GWD683MKM":88},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":60,"commitSha":61,"skillDirectory":62,"forksCount":14,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6EX1C2RZFF01C1GWD683MKM","sentence-transformers",null,"Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.","henriquescastilho\u002Fmy-claude","henriquescastilho","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153296945?v=4","01M6ESH7EXPXTH9CVQ4K16SRG8","Henrique Scheer","01M6ESH7JHEK2PTNNH4HEH787Z",0,"MIT","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-code-templates\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Frag-sentence-transformers\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"Transformers","sentence transformers","clustering","research","embedding","domain-specific","domain","multilingual","Art","use","text","rag","image","multimodal","embeddings","search","framework","state","tasks","semantic search","2026-11-10 10:12:30",false,"---\nname: sentence-transformers\ndescription: Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Sentence Transformers, Embeddings, Semantic Similarity, RAG, Multilingual, Multimodal, Pre-Trained Models, Clustering, Semantic Search, Production]\ndependencies: [sentence-transformers, transformers, torch]\n---\n\n# Sentence Transformers - State-of-the-Art Embeddings\n\nPython framework for sentence and text embeddings using transformers.\n\n## When to use Sentence Transformers\n\n**Use when:**\n- Need high-quality embeddings for RAG\n- Semantic similarity and search\n- Text clustering and classification\n- Multilingual embeddings (100+ languages)\n- Running embeddings locally (no API)\n- Cost-effective alternative to OpenAI embeddings\n\n**Metrics**:\n- **15,700+ GitHub stars**\n- **5000+ pre-trained models**\n- **100+ languages** supported\n- Based on PyTorch\u002FTransformers\n\n**Use alternatives instead**:\n- **OpenAI Embeddings**: Need API-based, highest quality\n- **Instructor**: Task-specific instructions\n- **Cohere Embed**: Managed service\n\n## Quick start\n\n### Installation\n\n```bash\npip install sentence-transformers\n```\n\n### Basic usage\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\n# Load model\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Generate embeddings\nsentences = [\n    \"This is an example sentence\",\n    \"Each sentence is converted to a vector\"\n]\n\nembeddings = model.encode(sentences)\nprint(embeddings.shape)  # (2, 384)\n\n# Cosine similarity\nfrom sentence_transformers.util import cos_sim\nsimilarity = cos_sim(embeddings[0], embeddings[1])\nprint(f\"Similarity: {similarity.item():.4f}\")\n```\n\n## Popular models\n\n### General purpose\n\n```python\n# Fast, good quality (384 dim)\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Better quality (768 dim)\nmodel = SentenceTransformer('all-mpnet-base-v2')\n\n# Best quality (1024 dim, slower)\nmodel = SentenceTransformer('all-roberta-large-v1')\n```\n\n### Multilingual\n\n```python\n# 50+ languages\nmodel = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')\n\n# 100+ languages\nmodel = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')\n```\n\n### Domain-specific\n\n```python\n# Legal domain\nmodel = SentenceTransformer('nlpaueb\u002Flegal-bert-base-uncased')\n\n# Scientific papers\nmodel = SentenceTransformer('allenai\u002Fspecter')\n\n# Code\nmodel = SentenceTransformer('microsoft\u002Fcodebert-base')\n```\n\n## Semantic search\n\n```python\nfrom sentence_transformers import SentenceTransformer, util\n\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Corpus\ncorpus = [\n    \"Python is a programming language\",\n    \"Machine learning uses algorithms\",\n    \"Neural networks are powerful\"\n]\n\n# Encode corpus\ncorpus_embeddings = model.encode(corpus, convert_to_tensor=True)\n\n# Query\nquery = \"What is Python?\"\nquery_embedding = model.encode(query, convert_to_tensor=True)\n\n# Find most similar\nhits = util.semantic_search(query_embedding, corpus_embeddings, top_k=3)\nprint(hits)\n```\n\n## Similarity computation\n\n```python\n# Cosine similarity\nsimilarity = util.cos_sim(embedding1, embedding2)\n\n# Dot product\nsimilarity = util.dot_score(embedding1, embedding2)\n\n# Pairwise cosine similarity\nsimilarities = util.cos_sim(embeddings, embeddings)\n```\n\n## Batch encoding\n\n```python\n# Efficient batch processing\nsentences = [\"sentence 1\", \"sentence 2\", ...] * 1000\n\nembeddings = model.encode(\n    sentences,\n    batch_size=32,\n    show_progress_bar=True,\n    convert_to_tensor=False  # or True for PyTorch tensors\n)\n```\n\n## Fine-tuning\n\n```python\nfrom sentence_transformers import InputExample, losses\nfrom torch.utils.data import DataLoader\n\n# Training data\ntrain_examples = [\n    InputExample(texts=['sentence 1', 'sentence 2'], label=0.8),\n    InputExample(texts=['sentence 3', 'sentence 4'], label=0.3),\n]\n\ntrain_dataloader = DataLoader(train_examples, batch_size=16)\n\n# Loss function\ntrain_loss = losses.CosineSimilarityLoss(model)\n\n# Train\nmodel.fit(\n    train_objectives=[(train_dataloader, train_loss)],\n    epochs=10,\n    warmup_steps=100\n)\n\n# Save\nmodel.save('my-finetuned-model')\n```\n\n## LangChain integration\n\n```python\nfrom langchain_community.embeddings import HuggingFaceEmbeddings\n\nembeddings = HuggingFaceEmbeddings(\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\n# Use with vector stores\nfrom langchain_chroma import Chroma\n\nvectorstore = Chroma.from_documents(\n    documents=docs,\n    embedding=embeddings\n)\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.embeddings.huggingface import HuggingFaceEmbedding\n\nembed_model = HuggingFaceEmbedding(\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\nfrom llama_index.core import Settings\nSettings.embed_model = embed_model\n\n# Use in index\nindex = VectorStoreIndex.from_documents(documents)\n```\n\n## Model selection guide\n\n| Model | Dimensions | Speed | Quality | Use Case |\n|-------|------------|-------|---------|----------|\n| all-MiniLM-L6-v2 | 384 | Fast | Good | General, prototyping |\n| all-mpnet-base-v2 | 768 | Medium | Better | Production RAG |\n| all-roberta-large-v1 | 1024 | Slow | Best | High accuracy needed |\n| paraphrase-multilingual | 768 | Medium | Good | Multilingual |\n\n## Best practices\n\n1. **Start with all-MiniLM-L6-v2** - Good baseline\n2. **Normalize embeddings** - Better for cosine similarity\n3. **Use GPU if available** - 10× faster encoding\n4. **Batch encoding** - More efficient\n5. **Cache embeddings** - Expensive to recompute\n6. **Fine-tune for domain** - Improves quality\n7. **Test different models** - Quality varies by task\n8. **Monitor memory** - Large models need more RAM\n\n## Performance\n\n| Model | Speed (sentences\u002Fsec) | Memory | Dimension |\n|-------|----------------------|---------|-----------|\n| MiniLM | ~2000 | 120MB | 384 |\n| MPNet | ~600 | 420MB | 768 |\n| RoBERTa | ~300 | 1.3GB | 1024 |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fsentence-transformers ⭐ 15,700+\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fsentence-transformers\n- **Docs**: https:\u002F\u002Fwww.sbert.net\n- **License**: Apache 2.0\n\n\n","# Sentence Transformers - State-of-the-Art Embeddings\n\nPython framework for sentence and text embeddings using transformers.\n\n## When to use Sentence Transformers\n\n**Use when:**\n- Need high-quality embeddings for RAG\n- Semantic similarity and search\n- Text clustering and classification\n- Multilingual embeddings (100+ languages)\n- Running embeddings locally (no API)\n- Cost-effective alternative to OpenAI embeddings\n\n**Metrics**:\n- **15,700+ GitHub stars**\n- **5000+ pre-trained models**\n- **100+ languages** supported\n- Based on PyTorch\u002FTransformers\n\n**Use alternatives instead**:\n- **OpenAI Embeddings**: Need API-based, highest quality\n- **Instructor**: Task-specific instructions\n- **Cohere Embed**: Managed service\n\n## Quick start\n\n### Installation\n\n```bash\npip install sentence-transformers\n```\n\n### Basic usage\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\n# Load model\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Generate embeddings\nsentences = [\n    \"This is an example sentence\",\n    \"Each sentence is converted to a vector\"\n]\n\nembeddings = model.encode(sentences)\nprint(embeddings.shape)  # (2, 384)\n\n# Cosine similarity\nfrom sentence_transformers.util import cos_sim\nsimilarity = cos_sim(embeddings[0], embeddings[1])\nprint(f\"Similarity: {similarity.item():.4f}\")\n```\n\n## Popular models\n\n### General purpose\n\n```python\n# Fast, good quality (384 dim)\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Better quality (768 dim)\nmodel = SentenceTransformer('all-mpnet-base-v2')\n\n# Best quality (1024 dim, slower)\nmodel = SentenceTransformer('all-roberta-large-v1')\n```\n\n### Multilingual\n\n```python\n# 50+ languages\nmodel = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')\n\n# 100+ languages\nmodel = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')\n```\n\n### Domain-specific\n\n```python\n# Legal domain\nmodel = SentenceTransformer('nlpaueb\u002Flegal-bert-base-uncased')\n\n# Scientific papers\nmodel = SentenceTransformer('allenai\u002Fspecter')\n\n# Code\nmodel = SentenceTransformer('microsoft\u002Fcodebert-base')\n```\n\n## Semantic search\n\n```python\nfrom sentence_transformers import SentenceTransformer, util\n\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# Corpus\ncorpus = [\n    \"Python is a programming language\",\n    \"Machine learning uses algorithms\",\n    \"Neural networks are powerful\"\n]\n\n# Encode corpus\ncorpus_embeddings = model.encode(corpus, convert_to_tensor=True)\n\n# Query\nquery = \"What is Python?\"\nquery_embedding = model.encode(query, convert_to_tensor=True)\n\n# Find most similar\nhits = util.semantic_search(query_embedding, corpus_embeddings, top_k=3)\nprint(hits)\n```\n\n## Similarity computation\n\n```python\n# Cosine similarity\nsimilarity = util.cos_sim(embedding1, embedding2)\n\n# Dot product\nsimilarity = util.dot_score(embedding1, embedding2)\n\n# Pairwise cosine similarity\nsimilarities = util.cos_sim(embeddings, embeddings)\n```\n\n## Batch encoding\n\n```python\n# Efficient batch processing\nsentences = [\"sentence 1\", \"sentence 2\", ...] * 1000\n\nembeddings = model.encode(\n    sentences,\n    batch_size=32,\n    show_progress_bar=True,\n    convert_to_tensor=False  # or True for PyTorch tensors\n)\n```\n\n## Fine-tuning\n\n```python\nfrom sentence_transformers import InputExample, losses\nfrom torch.utils.data import DataLoader\n\n# Training data\ntrain_examples = [\n    InputExample(texts=['sentence 1', 'sentence 2'], label=0.8),\n    InputExample(texts=['sentence 3', 'sentence 4'], label=0.3),\n]\n\ntrain_dataloader = DataLoader(train_examples, batch_size=16)\n\n# Loss function\ntrain_loss = losses.CosineSimilarityLoss(model)\n\n# Train\nmodel.fit(\n    train_objectives=[(train_dataloader, train_loss)],\n    epochs=10,\n    warmup_steps=100\n)\n\n# Save\nmodel.save('my-finetuned-model')\n```\n\n## LangChain integration\n\n```python\nfrom langchain_community.embeddings import HuggingFaceEmbeddings\n\nembeddings = HuggingFaceEmbeddings(\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\n# Use with vector stores\nfrom langchain_chroma import Chroma\n\nvectorstore = Chroma.from_documents(\n    documents=docs,\n    embedding=embeddings\n)\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.embeddings.huggingface import HuggingFaceEmbedding\n\nembed_model = HuggingFaceEmbedding(\n    model_name=\"sentence-transformers\u002Fall-mpnet-base-v2\"\n)\n\nfrom llama_index.core import Settings\nSettings.embed_model = embed_model\n\n# Use in index\nindex = VectorStoreIndex.from_documents(documents)\n```\n\n## Model selection guide\n\n| Model | Dimensions | Speed | Quality | Use Case |\n|-------|------------|-------|---------|----------|\n| all-MiniLM-L6-v2 | 384 | Fast | Good | General, prototyping |\n| all-mpnet-base-v2 | 768 | Medium | Better | Production RAG |\n| all-roberta-large-v1 | 1024 | Slow | Best | High accuracy needed |\n| paraphrase-multilingual | 768 | Medium | Good | Multilingual |\n\n## Best practices\n\n1. **Start with all-MiniLM-L6-v2** - Good baseline\n2. **Normalize embeddings** - Better for cosine similarity\n3. **Use GPU if available** - 10× faster encoding\n4. **Batch encoding** - More efficient\n5. **Cache embeddings** - Expensive to recompute\n6. **Fine-tune for domain** - Improves quality\n7. **Test different models** - Quality varies by task\n8. 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Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fwhat-if-oracle\u002FSKILL.md",[177],{"categoryNo":178,"categoryName":179,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000023","Skill与智能体开发",[181,182,183,184,185,186,187,188,189,190,191,123,124,192,193,194,195,196,197,198],"oracle","what if oracle","go","run","ux","futures","analysis","explore","decision","branch","question","idea","exploration","options","stress-test","strategies","scenario","stress",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":215,"publishedAt":39},"01M6EZZCXAEB49RXGQCJX5E8GB","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fvenue-templates\u002FSKILL.md",[205,208,211,212],{"categoryNo":206,"categoryName":207,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":209,"categoryName":210,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":178,"categoryName":179,"taxonomyType":98,"taxonomyTypeDesc":99},{"categoryNo":213,"categoryName":214,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000036","内容创作者",[216,217,22,218,219,220,221,222,120,223,123,124,125,224,225,226,227,228,170,229],"templates","venue templates","guidelines","NeurIPS","ICML","LaTeX","science","access","proposals","Academic","requirements","formatting","journal","chi"]