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  # Model Card
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  mStyleDistance is a **multilingual style embedding model** that aims to embed texts with similar writing styles closely and different styles far apart, regardless of content and regardless of language. You may find this model useful for stylistic analysis of multilingual text, clustering, authorship identfication and verification tasks, and automatic style transfer evaluation. The model is described in the paper [StyleDistance/mstyledistance](https://hf.co/papers/2502.15168).
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  This model is an multilingual version of the English-only [StyleDistance](https://huggingface.co/StyleDistance/styledistance) model.
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  print(cos_sim(input, others))
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  ```
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- ## Project page
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- For further information, please check the project page at https://huggingface.co/StyleDistance/mstyledistance.
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  ## Trained with DataDreamer
 
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  # Model Card
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+ This repository contains the model introduced in [mStyleDistance: Multilingual Style Embeddings and their Evaluation](https://hf.co/papers/2502.15168).
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  mStyleDistance is a **multilingual style embedding model** that aims to embed texts with similar writing styles closely and different styles far apart, regardless of content and regardless of language. You may find this model useful for stylistic analysis of multilingual text, clustering, authorship identfication and verification tasks, and automatic style transfer evaluation. The model is described in the paper [StyleDistance/mstyledistance](https://hf.co/papers/2502.15168).
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  This model is an multilingual version of the English-only [StyleDistance](https://huggingface.co/StyleDistance/styledistance) model.
 
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  print(cos_sim(input, others))
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  ```
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+ ---
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+ ## Citation
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+
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+ ```latex
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+ @misc{qiu2025mstyledistancemultilingualstyleembeddings,
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+ title={mStyleDistance: Multilingual Style Embeddings and their Evaluation},
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+ author={Justin Qiu and Jiacheng Zhu and Ajay Patel and Marianna Apidianaki and Chris Callison-Burch},
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+ year={2025},
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+ eprint={2502.15168},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2502.15168},
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+ }
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+ ```
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  ---
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  ## Trained with DataDreamer