Update README.md

README.md

@@ -2,17 +2,22 @@
 language: fr        # <-- my language
 widget:
  - text: "J'aime ta coiffure"
+   example_title: "NOT TOXIC 1" 
  - text: "Va te faire foutre"
+   example_title: "TOXIC 1"
  - text: "Quel mauvais temps, n'est-ce pas ?"
+   example_title: "NOT TOXIC 2" 
  - text: "J'espère que tu vas mourir, connard !"
+   example_title: "TOXIC 2"
  - text: "j'aime beaucoup ta veste"   
+   example_title: "NOT TOXIC 3"
 
 license: other
 ---
 This model was trained for toxicity labeling. Label_1 means TOXIC, Label_0 means NOT TOXIC
 
-The model was fine-tuned based off the CamemBERT language model https://huggingface.co/camembert-base .
+The model was fine-tuned based off [the CamemBERT language model](https://huggingface.co/camembert-base).
 
 The accuracy is 93% on the test split during training and 79% on a manually picked (and thus harder) sample of 200 sentences (100 label 1, 100 label 0) at the end of the training.
 
-The model was finetuned on 32k sentences. The train data was the translations of the english data (around 30k sentences) from https://github.com/s-nlp/multilingual_detox with https://huggingface.co/Helsinki-NLP/opus-mt-en-fr and the data from the jigsaw dataset on kaggle https://www.kaggle.com/competitions/jigsaw-multilingual-toxic-comment-classification/data .
+The model was finetuned on 32k sentences. The train data was the translations of the English data (around 30k sentences) from [the multilingual_detox dataset](https://github.com/s-nlp/multilingual_detox) by [Skolkovo Institute](https://huggingface.co/SkolkovoInstitute) using [the opus-mt-en-fr translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) by [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) and the data from [the jigsaw dataset](https://www.kaggle.com/competitions/jigsaw-multilingual-toxic-comment-classification/data) on kaggle.