Update README.md

README.md

@@ -39,7 +39,7 @@ T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervis
 ### Tokenization
 Since, T5 is a text-to-text model, the labels of the dataset are converted as follows:
 For each example, a sentence as been formed as **"mnli premise: " + mnli_premise + "hypothesis: " + mnli_hypothesis** and fed to the tokenizer to get the **input_ids** and **attention_mask**.
-For each label, label is choosen as **"entailment"** if label is 1, else label is **"not_entailment"** and tokenized to get **input_ids** and **attention_mask** . 
+For each label, target is choosen as **"entailment"** if label is 0, else it is **"neutral"** if label is 1, else it is **"contradiction"**  and tokenized to get **input_ids** and **attention_mask** . 
   During training, these inputs_ids having **pad** token are replaced with -100 so that loss is not calculated for them. Then these input ids are given as labels, and above attention_mask of labels
   is given as decoder attention mask.