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
Code and some results for comparing with other embedding models on multilingual data
#23
by Yannael - opened
I made some comparisons of BGE-M3, E5-large, E5-mistral, nomic-embed, and OpenAI v3 for a multilingual data retrieval task, see code here https://github.com/Yannael/multilingual-embeddings, and summary article there https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05
Thanks for your experiments! We will share these links in the README