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  This is a variant of the [google/mt5-base](https://huggingface.co/google/mt5-base) model, in which Ukrainian and 9% English words remain.
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  This model has 252M parameters - 43% of the original size.
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  Special thanks for the practical example and inspiration: [cointegrated ](https://huggingface.co/cointegrated)
 
 
 
 
 
 
 
 
 
 
 
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  This is a variant of the [google/mt5-base](https://huggingface.co/google/mt5-base) model, in which Ukrainian and 9% English words remain.
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  This model has 252M parameters - 43% of the original size.
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  Special thanks for the practical example and inspiration: [cointegrated ](https://huggingface.co/cointegrated)
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+
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+ ## Citing & Authors
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+ ```
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+ @misc{Uaritm,
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+ title={SetFit: Classification of medical texts},
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+ author={Vitaliy Ostashko},
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+ year={2022},
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+ url={https://esemi.org},
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+ }
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+ ```