Update README.md

README.md

@@ -33,8 +33,15 @@ alt="drawing" width="600"/>
 8. [Citation](#citation)
 9. [Model Card Authors](#model-card-authors)
 # my fine tuned model
->This model is fine tuned to generate a question with answers from a context , why is can be very usful this can help you to generate a dataset from a book article any thing you would to make from it dataset and train another model on this dataset 
-
+>This model is fine tuned to generate a question with answers from a context , why that can be very usful this can help you to generate a dataset from a book article any thing you would to make from it dataset and train another model on this dataset , give the model any context with pre prometed of quation you want + context and it will extarct question + answer for you 
+this are promted i use
+>[ "which", "how", "when", "where", "who", "whom", "whose", "why",
+    "which", "who", "whom", "whose", "whereas",
+    "can", "could", "may", "might", "will", "would", "shall", "should",
+    "do", "does", "did", "is", "are", "am", "was", "were", "be", "being", "been",
+    "have", "has", "had", "if", "is", "are", "am", "was", "were", "do", "does", "did", "can", "could",
+    "will", "would", "shall", "should", "might", "may", "must",
+    "may", "might", "must"]
 # TL;DR
 
 If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages.