Text Ranking
Transformers
Safetensors
sentence-transformers
qwen2
text-generation
text-embeddings-inference
Instructions to use mixedbread-ai/mxbai-rerank-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mixedbread-ai/mxbai-rerank-base-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/mxbai-rerank-base-v2") model = AutoModelForCausalLM.from_pretrained("mixedbread-ai/mxbai-rerank-base-v2") - sentence-transformers
How to use mixedbread-ai/mxbai-rerank-base-v2 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("mixedbread-ai/mxbai-rerank-base-v2") 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) - Notebooks
- Google Colab
- Kaggle
ouput score is logit?
#6 opened about 1 year ago
by
youli23
How to convert this model to onnx weight
#5 opened about 1 year ago
by
youli23
How to SFT on this model?
#4 opened about 1 year ago
by
youli23