Update README.md

README.md

@@ -41,7 +41,7 @@ $$acc=\frac{1}{|\mathcal{O}|}\sum_{i\in\mathcal{O}}\sum_{0\leq l < 5}p_{i,l}\hat
 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.
 
 #### 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.
+[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.
 
 How to use DistilCamemBERT-Sentiment
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