Rut5 Base Detox

🚀 Russian Text Detoxification Model

This is a detoxification baseline model that addresses the problem of converting Russian toxic text into non - toxic text, trained on specific competition data to provide a practical solution for text detoxification.

🚀 Quick Start

This is the detoxification baseline model trained on the train part of "RUSSE 2022: Russian Text Detoxification Based on Parallel Corpora" competition. The source sentences are Russian toxic messages from Odnoklassniki, Pikabu, and Twitter platforms. The base model is ruT5.

💻 Usage Examples

Basic Usage

from transformers import T5ForConditionalGeneration, AutoTokenizer

base_model_name = 'ai-forever/ruT5-base'
model_name = 's-nlp/ruT5-base-detox'

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

input_ids = tokenizer.encode('Это полная хуйня!', return_tensors='pt')
output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)
# Это полный бред!

📚 Documentation

Model Information

📄 License

This model is licensed under the OpenRAIL++ License, which supports the development of various technologies—both industrial and academic—that serve the public good.

📚 Citation

@article{dementievarusse,
  title={RUSSE-2022: Findings of the First Russian Detoxification Shared Task Based on Parallel Corpora},
  author={Dementieva, Daryna and Logacheva, Varvara and Nikishina, Irina and Fenogenova, Alena and Dale, David and Krotova, Irina and Semenov, Nikita and Shavrina, Tatiana and Panchenko, Alexander}
}