Text Ranking
sentence-transformers
Safetensors
Transformers
multilingual
t5gemma2
text2text-generation
reranker
encoder-decoder
FBNL
matryoshka
retrieval
RAG
Instructions to use KaLM-Embedding/KaLM-Reranker-V1-Nano-R2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KaLM-Embedding/KaLM-Reranker-V1-Nano-R2 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("KaLM-Embedding/KaLM-Reranker-V1-Nano-R2") 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) - Transformers
How to use KaLM-Embedding/KaLM-Reranker-V1-Nano-R2 with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Nano-R2") model = AutoModelForMultimodalLM.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Nano-R2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -56,7 +56,7 @@ All results below use the same default compression ratio, `r=4`. KaLM-Reranker-V
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#### BEIR — Multi-domain Reranking
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| Model | KaLM-Reranker-V1 | KaLM-Reranker-V1-R2 |
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| Nano | 57.41 | **58.54** | **+1.13** |
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| Small | 60.01 | **61.07** | **+1.06** |
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#### MIRACL — Multilingual Reranking
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| Model | KaLM-Reranker-V1 | KaLM-Reranker-V1-R2 |
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| Nano | 62.08 | **71.11** | **+9.03** |
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| Small | 66.89 | **74.06** | **+7.17** |
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url={https://arxiv.org/abs/2606.22807},
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}
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@
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title={KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model},
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author={Xinping Zhao and Xinshuo Hu and Zifei Shan and Shouzheng Huang and Yao Zhou and Xin Zhang and Zetian Sun and Zhenyu Liu and Dongfang Li and Xinyuan Wei and Youcheng Pan and Yang Xiang and Meishan Zhang and Haofen Wang and Jun Yu and Baotian Hu and Min Zhang},
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booktitle={The Fourteenth International Conference on Learning Representations},
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year={2026},
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}
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@misc{hu2025kalmembedding,
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#### BEIR — Multi-domain Reranking
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| Model | KaLM-Reranker-V1 | KaLM-Reranker-V1-R2 | Delta |
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|:--|--:|--:|--:|
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| Nano | 57.41 | **58.54** | **+1.13** |
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| Small | 60.01 | **61.07** | **+1.06** |
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#### MIRACL — Multilingual Reranking
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| Model | KaLM-Reranker-V1 | KaLM-Reranker-V1-R2 | Delta |
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|:--|--:|--:|--:|
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| Nano | 62.08 | **71.11** | **+9.03** |
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| Small | 66.89 | **74.06** | **+7.17** |
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url={https://arxiv.org/abs/2606.22807},
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}
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@misc{zhao2026kalmembeddingv2,
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title={KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model},
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author={Xinping Zhao and Xinshuo Hu and Zifei Shan and Shouzheng Huang and Yao Zhou and Xin Zhang and Zetian Sun and Zhenyu Liu and Dongfang Li and Xinyuan Wei and Youcheng Pan and Yang Xiang and Meishan Zhang and Haofen Wang and Jun Yu and Baotian Hu and Min Zhang},
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year={2026},
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eprint={2506.20923},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2506.20923},
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}
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@misc{hu2025kalmembedding,
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