|
--- |
|
language: fr |
|
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 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 [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. |