Update README.md

README.md

@@ -36,9 +36,9 @@ Benchmark
 This model is compared to 3 reference models (see below). As each model doesn't have the same definition of targets, we detail the performance measure used for each of them. For the mean inference time measure, an **AMD Ryzen 5 4500U @ 2.3GHz with 6 cores** was used.
 
 #### bert-base-multilingual-uncased-sentiment
-[nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) based on BERT model in multilingual and uncased version. This sentiment analyzer is trained on Amazon review like our model, then the targets and their definition are the same. In order to be robust to +/-1 star estimation errors, we will take the following definition as a performance measure:
-$$acc=\frac{1}{|\mathcal{O}|}\sum_{i\in\mathcal{O}}\sum_{0\leq < 5l}p_{i,l}\hat{p}_{i,l},$$
-where $$\mathcal{O}$$ are the observation of test dataset, $$p_l\in\{0,1\}$$ is equal at 1 for the true label and $$\hat{p}_l$$ the probabilite estimated for the l-th label.
+[nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) is based on BERT model in multilingual and uncased version. This sentiment analyzer is trained on Amazon review like our model, then the targets and their definition 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 $\mathcal{O}$ are the observation of test dataset, $p_l\in\{0,1\}$ is equal at 1 for the true label and $\hat{p}_l$ the probabilite estimated for the l-th label.
 
 #### [tf-allociné](https://huggingface.co/tblard/tf-allocine) and [barthez-sentiment-classification](https://huggingface.co/moussaKam/barthez-sentient-classification)