
This is a variant of the [google/mt5-base](https://huggingface.co/google/mt5-base) model, in which Ukrainian and 9% English words remain.
This model has 252M parameters - 43% of the original size.
Special thanks for the practical example and inspiration: [cointegrated ](https://huggingface.co/cointegrated)

## Citing & Authors
```
@misc{Uaritm,
      title={SetFit: Classification of medical texts}, 
      author={Vitaliy Ostashko},
      year={2022},
      url={https://esemi.org}
}
```

{"language": ["uk", "en", "multilingual"], "license": "mit", "tags": ["ukrainian", "english"]}
transformers pytorch t5 text2text-generation ukrainian english uk en multilingual license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #ukrainian #english #uk #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


This is a variant of the google/mt5-base model, in which Ukrainian and 9% English words remain.
This model has 252M parameters - 43% of the original size.
Special thanks for the practical example and inspiration: cointegrated 

## Citing & Authors


## Citing & Authors
TAGS
#transformers #pytorch #t5 #text2text-generation #ukrainian #english #uk #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ## Citing & Authors
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #ukrainian #english #uk #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
## Citing & Authors