language: multilingual
widget:
- text: J'aime ta coiffure
- text: Va te faire foutre
- text: Quel mauvais temps, n'est-ce pas ?
- text: J'espère que tu vas mourir, connard !
- text: j'aime beaucoup ta veste
- text: Guten morgen, meine Liebe
- text: Ich scheiß drauf.
- text: Ich liebe dich
- text: Ich hab die Schnauze voll von diesen Irren.
- text: Ich wünsche Ihnen einen schönen Tag!
- text: Сука тупая
- text: Какая прекрасная погода!
- text: Я ненавижу тебя козёл!
- text: Хлеб всему голова
- text: Вот же ублюдок....
- text: Go fuck yoursefl, asshole
- text: I don't really like this idea
- text: Look at this dickhead tho
- text: Usually, she is more open about that
- text: Why you have to always fuck everything up????
- text: I like this car
license: other
This model was trained for multilingual toxicity labeling. Label_1 means TOXIC, Label_0 means NOT TOXIC.
The model was fine-tuned based off the xlm_roberta_base model for 4 languages: EN, RU, FR, DE
The validation accuracy is 92%.
The model was finetuned on the total sum of 100933k sentences. The train data for English and Russian came from https://github.com/s-nlp/multilingual_detox, French data comprised the translated to French data from https://github.com/s-nlp/multilingual_detox as well as all the French data from the Jigsaw dataset, the German data was similarly composed using translations and semi-manual data collection techniquies, in particular for offensive words and phrases were crawled the dict.cc dictionary (https://www.dict.cc/) and the Reverso Context (https://context.reverso.net/translation/).