Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/orchestra-research/ai-research-skills --skill sentence-transformersAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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-transformersBasic 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
- 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
