Edit model card

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 provided from Sber.

How to use

from transformers import T5ForConditionalGeneration, AutoTokenizer

base_model_name = 'sberbank-ai/ruT5-base'
model_name = 'SkolkovoInstitute/ruT5-base-detox'

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
Downloads last month
5,000
Safetensors
Model size
223M params
Tensor type
F32
·

Dataset used to train s-nlp/ruT5-base-detox