Update README.md

README.md

@@ -38,7 +38,7 @@ This model is compared to 3 reference models (see below). As each model doesn't
 #### bert-base-multilingual-uncased-sentiment
 [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews similarly to our model, hence the targets and their definitions are the same. In order to be robust to +/-1 star estimation errors, we will take the following definition as a performance measure:
 $$acc=\frac{1}{|\mathcal{O}|}\sum_{i\in\mathcal{O}}\sum_{0\leq l < 5}p_{i,l}\hat{p}_{i,l}$$
-where $\mathcal{O}$ is the test set of the observations, $p_l\in\{0,1\}$ is equal to 1 for the true label and $\hat{p}_l$ is the estimated probability for the l-th label.
+where $\mathcal{O}$ is the test set of the observations, $p_l\in\{0,1\}$ is equal to 1 for the true label and 0 otherwise and $\hat{p}_l$ is the estimated probability for the l-th label.
 
 #### tf-allociné and barthez-sentiment-classification
 [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) based on [CamemBERT](https://huggingface.co/camembert-base) model and [moussaKam/barthez-sentiment-classification](https://huggingface.co/moussaKam/barthez-sentiment-classification) based on [BARThez](https://huggingface.co/moussaKam/barthez) use the same bi-class definition between them. To bring this back to a two-class problem, we will only consider the *"1 star"* and *"2 stars"* labels for the *negative* sentiments and *"4 stars"* and *"5 stars"* for *positive* sentiments. We exclude the *"3 stars"* which can be interpreted as a *neutral* class. In this context, the problem of +/-1 star estimation errors disappears. Then we use the classical accuracy definition.