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.
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
# 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
# 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
# 50+ languages
model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
# 100+ languages
model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
Domain-specific
# Legal domain
model = SentenceTransformer('nlpaueb/legal-bert-base-uncased')
# Scientific papers
model = SentenceTransformer('allenai/specter')
# Code
model = SentenceTransformer('microsoft/codebert-base')
Semantic search
from 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)
from 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
from 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
from 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
