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---
language: multilingual # <-- my language
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/). |