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
Are the output vectors normalized?
#96
by michael-newsrx-com - opened
Are the output vectors normalized?
Can I use them as is with qdrant and cosine similarity checks?
I am wanting to use the embeddings with qdrant, which has different options for similarity comparisons depending on "cosine" vs "normalized" and such.
Hi, I am using this model with Qdrant without any problem. I got same results with "normalized" embeddings
michael-newsrx-com changed discussion status to closed