Update README.md

README.md

@@ -23,6 +23,32 @@ Project: https://github.com/Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Au
 Leaderboard: https://eval.ai/web/challenges/challenge-page/503/leaderboard/1347
 
 In this repository, we trained the DeBERTa-V2-XXLarge on the sentence pair constructed by our AMR-LE. We use AMR with two logical equivalence laws `(Contraposition law, Double negation law)` to construct two different logical equivalence/inequivalence sentences.
+## How to interact model in this web page?
+Some test examples that you may copy and paste them into the right side user input area.
+The expected answer for the following example is they are logically inequivalent which is 0. Use constraposition law `(If A then B <=> If not B then not A)` to show that following example is false.
+```
+If Alice is happy, then Bob is smart.
+If Alice is not happy, then Bob is smart.
+```
+
+The expected answer for the following example is they are logically equivalent which is 1. Use constraposition law `(If A then B <=> If not B then not A)` to show that following example is true.
+```
+If Alice is happy, then Bob is smart.
+If Bob is not smart, then Alice is not happy.
+```
+
+The expected answer for the following example is they are logically inequivalent which is 0. Use double negation law `(A <=> not not A)` to show that following example is false.
+```
+Alice is happy.
+Alice is not happy.
+```
+
+The expected answer for the following example is they are logically equivalent which is 1. Use constraposition law `(A <=> not not A)` to show that following example is true.
+```
+Alice is happy.
+Alice is not sad.
+```
+
 ## How to load the model weight?
 ```
 from transformers import AutoModel