Update README.md

README.md

@@ -20,6 +20,11 @@ You can find [here](https://huggingface.co/google/flan-t5-base?text=Premise%3A++
 Our motivations for building **T5ForSequenceClassification** is that the full original T5 architecture is not needed for most NLU tasks. Indeed, NLU tasks generally do not require to generate text and thus a large decoder is unnecessary.
 By removing the decoder we can *half the original number of parameters* (thus half the computation cost) and *efficiently optimize* the network for the given task.
 
+## Table of Contents
+
+1. [Why use T5ForSequenceClassification?](##why-use-t5forsequenceclassification?)
+2. [T5ForClassification vs T5](##t5forclassification-vs-t5)
+
 ## Why use T5ForSequenceClassification?
 Models based on the [BERT](https://huggingface.co/bert-large-uncased) architecture like [RoBERTa](https://huggingface.co/roberta-large) and [DeBERTa](https://huggingface.co/microsoft/deberta-v2-xxlarge) have shown very strong performance on sequence classification task and are still widely used today.
 However, those models only scale up to ~1.5B parameters (DeBERTa xxlarge) resulting in a limited knowledge compare to bigger models.