[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EDXDMWMFNQJ8WR5BQRGY6F":3,"skill-same:01M6EDXDMWMFNQJ8WR5BQRGY6F":110},{"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":63,"repositoryHtmlUrl":64,"repositoryHomepageUrl":6,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6EDXDMWMFNQJ8WR5BQRGY6F","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.","luokai0\u002Fai-agent-skills-by-luo-kai","luokai0","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F266954973?v=4","01M6E16VNT42JB8B3X3254QWJ7","luo kai","01M6E16VTFVP6S5XJF1V1MPV90",14,"MIT","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F18-ai-agents-and-automation (by Luo Kai)\u002F16-other-agents\u002Fsentence-transformers\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"sentence transformers","Transformers","Art","embedding","retrieval","similarity","generation","domain-specific","domain","multilingual","models","image","use","text","embeddings","search","state","tasks","semantic search","rag","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: luo-kai\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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