This is the detoxification baseline model trained on the [train](https://github.com/skoltech-nlp/russe_detox_2022/blob/main/data/input/train.tsv) 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](https://huggingface.co/sberbank-ai/ruT5-base) provided from Sber.

**How to use**
```python
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)
```
{"language": ["ru"], "license": "openrail++", "tags": ["text-generation-inference"], "datasets": ["s-nlp/ru_paradetox"]}
transformers pytorch safetensors t5 text2text-generation text-generation-inference ru dataset:s-nlp/ru_paradetox license:openrail++ autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #text-generation-inference #ru #dataset-s-nlp/ru_paradetox #license-openrail++ #autotrain_compatible #endpoints_compatible #region-us 

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

TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #text-generation-inference #ru #dataset-s-nlp/ru_paradetox #license-openrail++ #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #text-generation-inference #ru #dataset-s-nlp/ru_paradetox #license-openrail++ #autotrain_compatible #endpoints_compatible #region-us 
