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
bashpip install sentence-transformers
Basic usage
pythonfrom 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-mpnet-base-v2') # Best quality (1024 dim, slower) model = SentenceTransformer('all-roberta-large-v1')
Multilingual
python# 50+ languages model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2') # 100+ languages model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
Domain-specific
python# Legal domain model = SentenceTransformer('nlpaueb/legal-bert-base-uncased') # Scientific papers model = SentenceTransformer('allenai/specter') # Code model = SentenceTransformer('microsoft/codebert-base')
Semantic search
pythonfrom sentence_transformers import SentenceTransformer, util model = SentenceTransformer('all-MiniLM-L6-v2') # Corpus corpus = [ "Python is a programming language", "Machine learning uses algorithms", "Neural networks are powerful" ] # Encode corpus corpus_embeddings = model.encode(corpus, convert_to_tensor=True) # Query query = "What is Python?" query_embedding = model.encode(query, convert_to_tensor=True) # Find most similar hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=3) print(hits)
Similarity computation
python# Cosine similarity similarity = util.cos_sim(embedding1, embedding2) # Dot product similarity = util.dot_score(embedding1, embedding2) # Pairwise cosine similarity similarities = util.cos_sim(embeddings, embeddings)
Batch encoding
python# Efficient batch processing sentences = ["sentence 1", "sentence 2", ...] * 1000 embeddings = model.encode( sentences, batch_size=32, show_progress_bar=True, convert_to_tensor=False # or True for PyTorch tensors )
Fine-tuning
pythonfrom sentence_transformers import InputExample, losses from torch.utils.data import DataLoader # Training data train_examples = [ InputExample(texts=['sentence 1', 'sentence 2'], label=0.8), InputExample(texts=['sentence 3', 'sentence 4'], label=0.3), ] train_dataloader = DataLoader(train_examples, batch_size=16) # Loss function train_loss = losses.CosineSimilarityLoss(model) # Train model.fit( train_objectives=[(train_dataloader, train_loss)], epochs=10, warmup_steps=100 ) # Save model.save('my-finetuned-model')
LangChain integration
pythonfrom langchain_community.embeddings import HuggingFaceEmbeddings embeddings = HuggingFaceEmbeddings( model_name="sentence-transformers/all-mpnet-base-v2" ) # Use with vector stores from langchain_chroma import Chroma vectorstore = Chroma.from_documents( documents=docs, embedding=embeddings )
LlamaIndex integration
pythonfrom llama_index.embeddings.huggingface import HuggingFaceEmbedding embed_model = HuggingFaceEmbedding( model_name="sentence-transformers/all-mpnet-base-v2" ) from llama_index.core import Settings Settings.embed_model = embed_model # Use in index index = VectorStoreIndex.from_documents(documents)
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
- Start with all-MiniLM-L6-v2 - Good baseline
- Normalize embeddings - Better for cosine similarity
- Use GPU if available - 10× faster encoding
- Batch encoding - More efficient
- Cache embeddings - Expensive to recompute
- Fine-tune for domain - Improves quality
- Test different models - Quality varies by task
- 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
