sentence-transformers

# 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 = cos_sim(embeddings[0], embeddings[1]) print(f"Similarity: {similarity.item():.4f}") ``` ## Popular models ### General purpose ```python # Fast, good quality (384 dim) model = SentenceTransformer('all-MiniLM-L6-v2') # Better quality (768 dim) model = SentenceTransformer('all-...