# rut5-base-detox-v2
Model was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.
* Task: `text2text generation`
* Type: `encoder-decoder`
* Tokenizer: `bpe`
* Dict size: `32 101`
* Num Parameters: `222 M`	

{"language": ["ru"], "tags": ["PyTorch", "Transformers"]}
transformers pytorch t5 text2text-generation PyTorch Transformers ru autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

# rut5-base-detox-v2
Model was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101'
* Num Parameters: '222 M'	

# rut5-base-detox-v2
Model was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101'
* Num Parameters: '222 M'
TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # rut5-base-detox-v2
Model was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101'
* Num Parameters: '222 M'
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# rut5-base-detox-v2
Model was fine-tuned from sberbank-ai/ruT5-base on parallel detoxification corpus.
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101'
* Num Parameters: '222 M'