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README.md
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## Multilingual-E5-large
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[Text Embeddings
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
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[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/abs/2402.05672).
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Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
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This model has 24 layers and the embedding size is 1024.
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For all labeled datasets, we only use its training set for fine-tuning.
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For other training details, please refer to our paper at [https://arxiv.org/pdf/
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## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
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If you find our paper or models helpful, please consider cite as follows:
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```
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@article{
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title={Text Embeddings
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author={Wang, Liang and Yang, Nan and Huang, Xiaolong and
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journal={arXiv preprint arXiv:
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year={
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}
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```
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## Multilingual-E5-large
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[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
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Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
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This model has 24 layers and the embedding size is 1024.
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For all labeled datasets, we only use its training set for fine-tuning.
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For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
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## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
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If you find our paper or models helpful, please consider cite as follows:
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```
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@article{wang2024multilingual,
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title={Multilingual E5 Text Embeddings: A Technical Report},
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author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
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journal={arXiv preprint arXiv:2402.05672},
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year={2024}
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}
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```
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