Update README.md

README.md

@@ -6,7 +6,7 @@ license: afl-3.0
 
 There are already some rule-based models that can accomplish this task, but I haven't seen any transformer-based models that can do so. Therefore, I trained this model based on `Bart-base` to transform QA pairs into declarative statements.
 
-I compared the my model with other rule base models, including 
+I compared the this model with other rule base models, including 
 
 > [paper1](https://aclanthology.org/D19-5401.pdf) (2019), which proposes **2 Encoder Pointer-Gen model**
 
@@ -55,9 +55,9 @@ Test on free_base testset
 | RBV2+XLNET   | 71.2 | 93.6    | 82.3    | 89.4    |
 | MPG          | 75.8 | 94.4    | 87.4    | 91.6    |
 
-There are reasons to believe that my model performs better than RBV2.
+There are reasons to believe that this model performs better than RBV2.
 
-To sum up,my model performs nearly as well as the SOTA rule-based model evaluated with BLEU and ROUGE score. However the sentence pattern is lack of diversity.
+To sum up, this model performs nearly as well as the SOTA rule-based model evaluated with BLEU and ROUGE score. However the sentence pattern is lack of diversity.
 
 (It's worth mentioning that even though I tried my best to conduct objective tests, the testsets I could find were more or less different from what they introduced in the paper.)