Update README.md

README.md

@@ -26,7 +26,7 @@ Training
 The training dataset consists of 500k examples of comments in English and 500k comments in French (translated by Google Translate), each annotated with a probablity toxicity severity. The dataset used is provided by [Jigsaw](https://jigsaw.google.com/approach/) as part of a Kaggle competition : [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification/data). As the score represents the probability of a toxicity mode, an optimization goal of cross-entropy type has been chosen:
 $$loss=l_{\mathrm{obscene}}+l_{\mathrm{sexual\_explicit}}+l_{\mathrm{identity\_attack}}+l_{\mathrm{insult}}+l_{\mathrm{threat}}$$
 with
-$$l_i=\frac{1}{\vert\mathcal{O}\vert}\sum_{o\in\mathcal{O}}\mathrm{score}_{i,o}\log(\sigma(\mathrm{logit}_{i,o}))$$
+$$l_i=\frac{-1}{\vert\mathcal{O}\vert}\sum_{o\in\mathcal{O}}\mathrm{score}_{i,o}\log(\sigma(\mathrm{logit}_{i,o}))+(\mathrm{score}_{i,o}-1)\log(1-\sigma(\mathrm{logit}_{i,o}))$$
 Where sigma is the sigmoid function and O represents the set of learning observations.
 
 Benchmark