# ruT5-large
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).

The model was trained by the [SberDevices](https://sberdevices.ru/).  
* Task: `text2text generation`
* Type: `encoder-decoder`
* Tokenizer: `bpe`
* Dict size: `32 101 `
* Num Parameters: `737 M`
* Training Data Volume `300 GB`

# Authors
+ NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam):
  + Dmitry Zmitrovich
 
# Cite us
```
@misc{zmitrovich2023family,
      title={A Family of Pretrained Transformer Language Models for Russian}, 
      author={Dmitry Zmitrovich and Alexander Abramov and Andrey Kalmykov and Maria Tikhonova and Ekaterina Taktasheva and Danil Astafurov and Mark Baushenko and Artem Snegirev and Tatiana Shavrina and Sergey Markov and Vladislav Mikhailov and Alena Fenogenova},
      year={2023},
      eprint={2309.10931},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/model-zoo"}
transformers pytorch t5 text2text-generation PyTorch Transformers ru arxiv:2309.10931 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

# ruT5-large
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.

The model was trained by the SberDevices.  
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101 '
* Num Parameters: '737 M'
* Training Data Volume '300 GB'

# Authors
+ NLP core team RnD Telegram channel:
  + Dmitry Zmitrovich
 
# Cite us

# ruT5-large
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.

The model was trained by the SberDevices.  
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101 '
* Num Parameters: '737 M'
* Training Data Volume '300 GB' # Authors
+ NLP core team RnD Telegram channel:
  + Dmitry Zmitrovich # Cite us
TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # ruT5-large
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.

The model was trained by the SberDevices.  
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101 '
* Num Parameters: '737 M'
* Training Data Volume '300 GB' # Authors
+ NLP core team RnD Telegram channel:
  + Dmitry Zmitrovich # Cite us
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# ruT5-large
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.

The model was trained by the SberDevices.  
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101 '
* Num Parameters: '737 M'
* Training Data Volume '300 GB'# Authors
+ NLP core team RnD Telegram channel:
  + Dmitry Zmitrovich# Cite us