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", device_map="auto") - 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
Merge branch 'main' of https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2
Browse files
README.md
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<sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</b></a> </sup>
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</p>
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# 🍞 mxbai-rerank-base-v2
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This is the base model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v2).
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```bibtex
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@article{li2025prorank,
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title={ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking},
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author={
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journal={arXiv preprint arXiv:2506.03487},
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year={2025}
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}
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<sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</b></a> </sup>
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</p>
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# 🍞 mxbai-rerank-base-v2 (a.k.a ProRank-0.5B)
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This is the base model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v2).
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```bibtex
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@article{li2025prorank,
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title={ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking},
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author={Xianming Li and Aamir Shakir and Rui Huang and Julius Lipp and Benjamin Clavié and Jing Li},
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journal={arXiv preprint arXiv:2506.03487},
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year={2025}
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}
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