readme: ---
  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/).