FRED T5 1.7B

🚀 FRED-T5 1.7B (Full-scale Russian Enhanced Denoisers T5)

FRED-T5 1.7B is a powerful language model for the Russian language, based on the T5 architecture. It offers high - performance language processing capabilities trained on a large - scale Russian corpus.

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 SberDevices.

📋 Model Information

🚀 Quick Start

The model is based on the T5 architecture, with specific training processes and configurations. More details can be found in the config.json file.

💻 Usage Examples

Basic Usage

import torch
from transformers import GPT2Tokenizer, T5ForConditionalGeneration 
tokenizer = GPT2Tokenizer.from_pretrained('ai-forever/FRED-T5-1.7B',eos_token='</s>')
model = T5ForConditionalGeneration.from_pretrained('ai-forever/FRED-T5-1.7B')
device='cuda'
model.to(device)

#Prefix <LM>
lm_text='<LM>Принялся Кутузов рассказывать свою историю как он сюда попал. Началось'
input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device)
outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True)
print(tokenizer.decode(outputs[0][1:]))

# print result: с того, что он был в армии, служил в артиллерии</s>.

#Prefix <SC1>
lm_text='<SC1>Принялся Кутузов рассказывать свою историю <extra_id_0>. Началось с того, что он был в армии, служил в артиллерии.'
input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device)
outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True)
print(tokenizer.decode(outputs[0][1:]))

#print result: '<extra_id_0>, как он воевал</s>'

# Prefix <SC5> 
lm_text='<SC5>Принялся Кутузов рассказывать свою историю <extra_id_0>. Началось с того, что он был в армии, служил в артиллерии.'
input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device)
outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True)
tokenizer.decode(outputs[0][1:])

#print result: '<extra_id_0>, как он стал генералом</s>'

📚 Documentation

The model was trained on a mixture of 7 denoisers similar to UL2 with several differences (https://arxiv.org/abs/2205.05131).

👨‍💻 Authors

• NLP core team RnD Telegram channel:
  • Dmitry Zmitrovich
  • Andrei Kalmykov
  • Vitaly Kadulin
  • Mikhail Novikov
  • Alexey Khoroshilov

Salute AI Community.

📄 License

This project is licensed under the Apache-2.0 license.

📖 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}
}