sentence-transformers

Q: How do I install sentence-transformers?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill sentence-transformers in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sentence-transformers/ (personal) or .claude/skills/sentence-transformers/ (project).

by orchestra-researchAI & Agents

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.

Installs: 964
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/orchestra-research/ai-research-skills --skill sentence-transformers

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads sentence-transformers automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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')

# 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)

Similarity computation

# 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

# 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

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
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

FAQ

What does the sentence-transformers skill do?

How do I install sentence-transformers?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill sentence-transformers in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sentence-transformers/ (personal) or .claude/skills/sentence-transformers/ (project).

Which AI agents can use sentence-transformers?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is sentence-transformers safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains sentence-transformers?

It is published by orchestra-research in orchestra-research/ai-research-skills. It has 964 recorded installs.