Update README.md

README.md

@@ -100,20 +100,20 @@ Token counts are white space based.
 
 ## Training results with comparison
 
-It achieves the following results on the test set:
+__mT5-based-azerbaijani-summarize__ model rouge scores on the test set:
 
-Rouge1: 39.4222
-Rouge2: 24.8624
-Rougel: 32.2487
+- Rouge1: 39.4222
+- Rouge2: 24.8624
+- Rougel: 32.2487
 
 For __Azerbaijani text summarization downstream task__, mT5-multilingual-XLSum has also been developed on the 45 languages of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset. For finetuning details and scripts,
 see the [paper](https://aclanthology.org/2021.findings-acl.413/) and the [official repository](https://github.com/csebuetnlp/xl-sum). .
 
-Scores on the XL-Sum test sets for Azerbaijani are as follows:
+__mT5_multilingual_XLSum__ modelrouge scores on the XL-Sum test set (only for Azerbaijani):
 
-Rouge1: 21.4227
-Rouge2: 9.5214
-Rougel: 19.3331
+- Rouge1: 21.4227
+- Rouge2: 9.5214
+- Rougel: 19.3331
 
 As seen from the numbers, __mT5-based-azerbaijani-summarize__ model achieves dramatically better performance than __mT5_multilingual_XLSum__.