KaLM-Embedding-Gemma3-12B-2511

Short Description

KaLM-Embedding-Gemma3-12B-2511 is a versatile and compact embedding model, which achieves SOTA performance in MMTEB (due to 11-2025).

MMTEB Evaluation Results

Rank (Borda)	Model	Mean (Task)	Mean (TaskType)	Bitext Mining	Classification	Clustering	Instruction Reranking	Multilabel Classification	Pair Classification	Reranking	Retrieval	STS
1	KaLM-Embedding-Gemma3-12B-2511	72.32	62.51	83.76	77.88	55.77	5.49	33.03	84.73	67.27	75.66	79.02
2	llama-embed-nemotron-8b	69.46	61.09	81.72	73.21	54.35	10.82	29.86	83.97	67.78	68.69	79.41
3	Qwen3-Embedding-8B	70.58	61.69	80.89	74.00	57.65	10.06	28.66	86.40	65.63	70.88	81.08
4	gemini-embedding-001	68.37	59.59	79.28	71.82	54.59	5.18	29.16	83.63	65.58	67.71	79.40
5	Qwen3-Embedding-4B	69.45	60.86	79.36	72.33	57.15	11.56	26.77	85.05	65.08	69.60	80.86
6	Qwen3-Embedding-0.6B	64.34	56.01	72.23	66.83	52.33	5.09	24.59	80.83	61.41	64.65	76.17
7	gte-Qwen2-7B-instruct	62.51	55.93	73.92	61.55	52.77	4.94	25.48	85.13	65.55	60.08	73.98
8	Linq-Embed-Mistral	61.47	54.14	70.34	62.24	50.60	0.94	24.77	80.43	64.37	58.69	74.86
9	multilingual-e5-large-instruct	63.22	55.08	80.13	64.94	50.75	-0.40	22.91	80.86	62.61	57.12	76.81
10	embeddinggemma-300m	61.15	54.31	64.40	60.90	51.17	5.61	24.82	81.40	63.25	62.49	74.73

Model Details

Model Size: 11.76B
Embedding Dimension: 3840
Max Input Tokens: 32k
MRL dimensions: 3840, 2048, 1024, 512, 256, 128, and 64
Pooling: lasttoken pooling

Citation

If you find this model useful, please consider giving a star and citation.

@misc{zhao2025kalmembeddingv2,
      title={KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model}, 
      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},
      year={2025},
      eprint={2506.20923},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.20923}, 
}
@misc{hu2025kalmembedding,
      title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model}, 
      author={Xinshuo Hu and Zifei Shan and Xinping Zhao and Zetian Sun and Zhenyu Liu and Dongfang Li and Shaolin Ye and Xinyuan Wei and Qian Chen and Baotian Hu and Haofen Wang and Jun Yu and Min Zhang},
      year={2025},
      eprint={2501.01028},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.01028}, 
}

Contact

If you encounter any issue, feel free to contact us via the email: yanshek.woo@gmail.com, xinpingzhao@slai.edu.cn

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