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---
license: mit
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: 'null'
- name: relevance
dtype: float64
splits:
- name: train
num_bytes: 9647396601
num_examples: 23670898
download_size: 6255637479
dataset_size: 9647396601
configs:
- config_name: default
data_files:
- split: train
path: Amazon-Reviews-2023/train-*
---
*The finetuning dataset is is available at this link:[KaLM-Embedding/KaLM-embedding-finetuning-data](https://huggingface.co/datasets/KaLM-Embedding/KaLM-embedding-finetuning-data).*
## Citation
If you find these datasets 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: <[email protected]>, <[email protected]>
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