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
32k-compatible
#3
by exoplanet - opened
Hi awesome bakers, great job with this (v2) reranker family. On your technical blog, "32k-compatible" is mentioned, but I couldn't figure out how this works in practice. Any pointers would be appreciated. Krispy regards.
hey @exoplanet , we've fine-tuned the model up to a context length of 8k but it generally supports 32k.
You can just pass max_length to the model during initialisation:
reranker = MxbaiRerankV2(..., max_length=32768)
juliuslipp changed discussion status to closed