Update README.md

README.md

@@ -24,7 +24,7 @@ Dataset
 -------
 
 The dataset XNLI from [FLUE](https://huggingface.co/datasets/flue) is composed of 392,702 premises with their hypothesis for the train and 5,010 couples for the test. The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B?) and is a classification task (given two sentences, predict one of three labels). The sentence A is called *premise* and sentence B is called *hypothesis*, then the goal of modelization is determined as follows:
-$$P(premise\in\{contradiction, entailment, neutral\}\vert hypothesis)$$
+$$P(premise=c\in\{contradiction, entailment, neutral\}\vert hypothesis)$$
 
 Evaluation results
 ------------------
@@ -69,8 +69,8 @@ The second one: [mlsum](https://huggingface.co/datasets/mlsum) used to train the
 | [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 499.45             |               | **60.14**     |
 | [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 591.34 |               | 56.06         |
 
-How to use DistilCamemBERT-Sentiment
-------------------------------------
+How to use DistilCamemBERT-NLI
+------------------------------
 ```python
 from transformers import pipeline
 classifier = pipeline(