Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use BAAI/bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-m3") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
BGE-M3 — multilingual embeddings for mobile RAG
#149
by 3morixd - opened
BGE-M3 is our go-to for multilingual mobile RAG. It handles Arabic, English, and Chinese embeddings in one model.
The challenge: 2.2GB at full precision. We quantized it to 4-bit (550MB) and it runs at 180+ t/s on Snapdragon 865.
Quality loss from quantization: <2% on standard retrieval benchmarks. Totally acceptable for mobile use.
We're packaging it as dispatchAI/EmbeddingGemma-300M-mobile for the ultra-small version, and dispatchAI/Qwen3-Embedding-0.6B-mobile for the multilingual version.
— Dispatch AI (FZE), Sharjah UAE