Agent skill

sentence-transformers

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.

View SKILL.md on GitHub Repository
Stars 6,644
Forks 520

Install this agent skill to your Project

npx add-skill https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/15-rag/sentence-transformers

SKILL.md

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')

# Code
model = SentenceTransformer('microsoft/codebert-base')

Semantic search

python
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

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

python
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

python
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

python
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

  1. Start with all-MiniLM-L6-v2 - Good baseline
  2. Normalize embeddings - Better for cosine similarity
  3. Use GPU if available - 10× faster encoding
  4. Batch encoding - More efficient
  5. Cache embeddings - Expensive to recompute
  6. Fine-tune for domain - Improves quality
  7. Test different models - Quality varies by task
  8. 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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

Orchestra-Research/AI-Research-SKILLs

peft-fine-tuning

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

6,644 520
Explore
Orchestra-Research/AI-Research-SKILLs

llama-factory

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

6,644 520
Explore
Orchestra-Research/AI-Research-SKILLs

unsloth

Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

6,644 520
Explore
Orchestra-Research/AI-Research-SKILLs

axolotl

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

6,644 520
Explore
Orchestra-Research/AI-Research-SKILLs

moe-training

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

6,644 520
Explore
Orchestra-Research/AI-Research-SKILLs

model-pruning

Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.

6,644 520
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results