Update README.md

README.md

@@ -20,19 +20,23 @@ This model adapt T5 on Arabic Language by pre-training T5 on ArabicWikipedia, Ma
 
 ##  Results on TyDi QA, HARD, Sentiment Analysis, Sarcasm Detection ( Best Score is highlighted in bold )
 
-|    Model            | <center>TyDi QA (Dev) | <center>HARD (Hotel Review) | <center>ArSarcasm-v2 (Sentiment Analysis)   | <center>ArSarcasm-v2 (Sarcasm Detection) |
-|----------------------|---------------|---------------------|-------------------------------------|----------------------------------|
-| AraT5-Base           |  <center>70.36/84.21  |<center>96.49|<center>69.7/72.63|<center>60.44|   
-| AraT5-Base-MSA       |  <center>70.90/84.00  |<center>**96.52**|<center>70.03/72.73|<center>60.69|
-| AraT5-Base-Tweets    |  <center>65.14/79.00  |<center>96.26|<center>70.67/73.52|<center>61.11|
-| mT5-Base             |  <center>72.20/84.13  |<center>96.24|<center>67.33/68.78|<center>52.18|
-| ArabicT5-Base        |  <center>70.79/84.76  |<center>96.36|<center>68.93/71.20|<center>58.93|
-| ArabicT5-Large       |  <center>73.29/86.08  |<center>96.40|<center>70.4/73.01|<center>59.79|
-| ArabicT5-xLarge      |  <center>**75.46/87.12**  |<center>96.50| <center>**72.23/75.17**|<center>**61.66**|
-
-Evaluation Metrics: TyDi QA (EM/F1), HARD (Accuracy), Sentiment Analysis (Accuracy / F1-PN positive-negative), Sarcasm Detection (F1-sarcastic).
-
-You can download the full details of our grid search for all models in all tasks above from this link : https://drive.google.com/file/d/1yR5sxwYZL-ugHGjeW0ueaiOIzZiOSPad/view?usp=sharing
+|    Model            | <center>TyDi QA| <center>HARD| <center>ArSarcasm-v2-Sentiment| <center>ArSarcasm-v2-Sarcasm| XL-SUM |
+|----------------------|---------------|---------------------|-------------------------------------|----------------------------------|----------------------------------
+| AraT5-Base           |  <center>70.36/84.21  |<center>96.49|<center>69.7/72.63|<center>60.44|<center>30.31|
+| AraT5-Base-MSA       |  <center>70.90/84.00  |<center>**96.52**|<center>70.03/72.73|<center>60.69|<center>27.36|
+| AraT5-Base-Tweets    |  <center>65.14/79.00  |<center>96.26|<center>70.67/73.52|<center>61.11|<center>25.08|
+| mT5-Base             |  <center>72.20/84.13  |<center>96.24|<center>67.33/68.78|<center>52.18|<center>25.68|
+| ArabicT5-Base        |  <center>70.79/84.76  |<center>96.36|<center>68.93/71.20|<center>58.93|<center>29.19|
+| ArabicT5-Large       |  <center>73.29/86.08  |<center>96.40|<center>70.4/73.01|<center>59.79|<center>30.30|
+| ArabicT5-xLarge      |  <center>**75.46/87.12**  |<center>96.50| <center>**72.23/75.17**|<center>**61.66**|<center>**31.70**|
+
+Evaluation Metrics: TyDi QA (EM/F1), HARD (Accuracy), Sentiment Analysis (Accuracy / F1-PN positive-negative), Sarcasm Detection (F1-sarcastic), XL-SUM (Rouge-L with Stemmer).
+
+You can download the full details of our grid search for all models in all tasks above from this link: https://github.com/salrowili/ArabicT5/raw/main/ArabicT5_Grid_Search.zip
+
+For the XL-Sum task, we choose our best run for each model using the eval set. We use the official evaluation script from XL-Sum, which uses the stemmer function, which may show better results than papers that don't use the stemmer function. The official XL-Sum paper uses a stemmer function.
+
+In our XL-Sum results, although we show that AraT5-Base exceeded our ArabicT5-Large, in most runs, our ArabicT5-Large shows better results, as you can see from our grid search file.
 
 # Speedup Results