sentence-transformers
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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentence-transformers --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Sentence Transformers - State-of-the-Art Embeddings Python framework for sentence and text embeddings using transformers. ## When to use Sentence Transformers **Use when:** - Need high-quality embeddings for RAG - Semantic similarity and search - Text clustering and classification - Multilingual embeddings (100+ languages) - Running embeddings locally (no API) - Cost-effective alternative to OpenAI embeddings **Metrics**: - **15,700+ GitHub stars** - **5000+ pre-trained models** - **100+ languages** supported - Based on PyTorch/Transformers **Use alternatives instead**: - **OpenAI Embeddings**: Need API-based, highest quality - **Instructor**: Task-specific instructions - **Cohere Embed**: Managed service ## Quick start ### Installation ```bash pip install sentence-transformers ``` ### Basic usage ```python from sentence_transformers import SentenceTransformer # Load model model = SentenceTransformer('all-MiniLM-L6-v2') # Generate embeddings sentences = [ "This is an example sentence", "Each sentence is converted to a vector" ] embeddings = model.encode(sentences) print(embeddings.shape) # (2, 384) # Cosine similarity from sentence_transformers.util import cos_sim similarity = co
- When to use Sentence Transformers
- Quick start
- Installation
- Basic usage
- Popular models
- General purpose
- Multilingual
- Domain-specific
- Semantic search
- Similarity computation
- Batch encoding
- Fine-tuning
- LangChain integration
- LlamaIndex integration
pip install sentence-transformers
What does the sentence-transformers skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentence-transformers --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.