# Sentence Transformers

Framework for state-of-the-art sentence, text, and image embeddings.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentence-transformers -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentence-transformers -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentence-transformers -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentence-transformers -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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-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')

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/15-rag/sentence-transformers/SKILL.md