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
Add base_model metadata (BAAI/bge-m3-unsupervised)
#145
by victor HF Staff - opened
This repo currently has no base_model set in its card metadata, so it doesn't appear in any model's tree:
base_model:
- BAAI/bge-m3-unsupervised
Setting this will surface the model in BAAI/bge-m3-unsupervised's Model Tree (under its quantizations/finetunes) and improve discoverability for people browsing from the base model.
Feel free to merge directly (or close).