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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