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
Production hybrid retrieval using bgem3
#115
by swapashtekar - opened
Hi,
What are the model inferencing options to use BGE-M3 model in production scenario? I am using model to obtain hybrid retrieval (both dense and sparse embeddings) as mentioned in milvus documentation.
Are there any ways where i can run bge-m3 model to get hybrid retrieval as mentioned in https://github.com/huggingface/text-embeddings-inference
Thank you.
Hi, I'm also curious about this as I have created a setup which works locally, but I'm struggling to get it working in production. What did you find?
If you're still looking for a production-level inference engine for BGE-M3 for hybrid retrieval, I've built this!
https://github.com/MauroCE/m3serve