Update README.md

README.md

@@ -57,7 +57,7 @@ This model is compared to 3 reference models (see below). As each model doesn't
 | [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) | 187.70 | 54.41 | 82.82 |
 
 #### 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 only the classical accuracy definition.
 
 | **model** | **time (ms)** | **exact accuracy (%)** |
 | :-------: | :-----------: | :--------------------: |