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

# 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')