Agent skill · research science · davila7
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.
Why this skill is useful
Provides domain-specific knowledge for generating high-quality embeddings and semantic similarity tasks that the AI wouldn't reliably generate on its own.
What it needs
Requires sentence-transformers, torch, transformers installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
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 Basic usage Popular models General purpose Multilingual Domain-specific Semantic search Similarity computation Batch encoding Fine-tuning LangChain integration LlamaIndex integration Model selection guide Model Dimensions Speed Quality Use Case ------- ------------ ------- --------- ---------- all-MiniLM-L6-v2 384 Fast Good General, prototyping all-mpnet-base-v2 768 Medium Better Production RAG all-roberta-large-v1 1024 Slow Best High accuracy needed paraphrase-multilingual 768 Medium Good Multilingual Best practices 1. Start with all-MiniLM-L6-v2 - Good baseline 2. Normalize embeddings - Better for cosine similarity 3. Use GPU if available - 10× faster encoding 4. Batch encoding - More efficient 5. Cache embeddings - Expensive to recompute 6. Fine-tune for domain - Improves quality 7. Test different models - Quality varies by task 8. Monitor memory - Large models need more RAM Performance Model Speed (sentences/sec) Memory Dimension ------- ---------------------- --------- ----------- MiniLM ~2000 120MB 384 MPNet ~600 420MB 768 RoBERTa ~300 1.3GB 1024 Resources GitHub: https://github.com/UKPLab/sentence-transformers ⭐ 15,700+ Models: https://huggingface.co/sentence-transformers Docs: https://www.sbert.net License: Apache 2.0
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/rag-sentence-transformers