Correct description for label

"Entail" means "to have as a logically necessary consequence." The original description for label got it backwards. The label "entail" means the premise entails the hypothesis, not the other way around; it's also the case for the other two labels. Consider this example:

Premise: A few people in a restaurant setting, one of them is drinking orange juice.
Hypothesis: The diners are at a restaurant.
Label: entailment

Obviously if you know the premise that "a few people [are] in a restaurant, one of them is drinking orange juice", it "has as a logically necessary consequence that "the diners are at a restaurant." However, if you know the hypothesis that they are at a restaurant, "one of them is drinking orange juice" is not its logically necessary consequence.

Please correct this in order not to mislead people on this model card.

README.md

@@ -131,7 +131,7 @@ The average token count for the premises and hypotheses are given below:
 
 - `premise`: a string used to determine the truthfulness of the hypothesis
 - `hypothesis`: a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
-- `label`: an integer whose value may be either _0_, indicating that the hypothesis entails the premise, _1_, indicating that the premise and hypothesis neither entail nor contradict each other, or _2_, indicating that the hypothesis contradicts the premise. Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`.
+- `label`: an integer whose value may be either _0_, indicating that the premise entails the hypothesis, _1_, indicating that the premise neither entails nor contradicts the hypothesis, or _2_, indicating that the premise contradicts the hypothesis. Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`.
 
 
 ### Data Splits