| name | sentence-transformers |
| description | 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. |
| category | llm-tools |
| version | 1.0.0 |
| author | Synthetic Sciences |
| license | MIT |
| tags | ["Sentence Transformers","Embeddings","Semantic Similarity","RAG","Multilingual","Multimodal","Pre-Trained Models","Clustering","Semantic Search","Production"] |
| dependencies | ["sentence-transformers","transformers","torch"] |
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
pip install sentence-transformers
Basic usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = [
"This is an example sentence",
"Each sentence is converted to a vector"
]
embeddings = model.encode(sentences)
print(embeddings.shape)
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item():.4f}")
Popular models
General purpose
model = SentenceTransformer('all-MiniLM-L6-v2')
model = SentenceTransformer()
model = SentenceTransformer()
