README.md

---
library_name: transformers
license: mit
datasets:
- textdetox/multilingual_paradetox
- chameleon-lizard/synthetic-multilingual-paradetox
language:
- ru
- en
- am
- uk
- de
- es
- ar
- hi
pipeline_tag: text2text-generation
---

# Model Card for Model ID

Finetune of the mt0-xl model for text detoxification task.


## Model Details

### Model Description

This is a finetune of mt0-xl model for text detoxification task. Can be used for synthetic data generation from toxic examples.

- **Developed by:** Nikita Sushko
- **Model type:** mt5-xl
- **Language(s) (NLP):** English, Russian, Ukranian, Amharic, German, Spanish, Chinese, Arabic, Hindi
- **License:** MIT
- **Finetuned from model:** mt0-xl

## Uses

This model is intended to be used as a text detoxification task in 9 languages: English, Russian, Ukranian, Amharic, German, Spanish, Chinese, Arabic, Hindi.

### Direct Use

The model may be directly used for text detoxification tasks.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import transformers

checkpoint = 'chameleon-lizard/detox-mt0-xl'

tokenizer = transformers.AutoTokenizer.from_pretrained(checkpoint)
model = transformers.AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype='auto', device_map="auto")

pipe = transformers.pipeline(
    "text2text-generation", 
    model=model, 
    tokenizer=tokenizer, 
    max_length=512,
    truncation=True,
)

language = 'English'
text = "You are a major fucking disappointment."
print(pipe('Write a non-toxic version of the following text in {language}: {text}')[0]['generated_text'])
# Resulting text: "You are a major disappointment.""
```

Be sure to prompt with the provided prompt format for the best performance. Failure to include target language may result in model responses be in random language.