Text Classification
sentence-transformers
PyTorch
ONNX
Safetensors
English
Chinese
xlm-roberta
mteb
text-embeddings-inference
Eval Results (legacy)
Instructions to use BAAI/bge-reranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-reranker-base with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("BAAI/bge-reranker-base") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Inference
- Notebooks
- Google Colab
- Kaggle
Languages covered?
#16
by oliviermills - opened
Where can we find the list of the languages covered. Can we assume performance based on the BGE-m3 languages?
Where can we find the list of the languages covered. Can we assume performance based on the BGE-m3 languages?
Answering my own question found this list here: https://huggingface.co/BAAI/bge-m3/discussions/29#65fab3238e807a4adbfa4f78
Pointing out low-resource languages will perform less well.
oliviermills changed discussion status to closed