Update README.md

README.md

@@ -1,9 +1,15 @@
 ---
+language:
+- en
+datasets:
+- SAMSum
+metrics:
+- ROUGE-L
 tags:
-- question answering
-- t5
-- declarative
-- text generation
+- Question answering
+- T5
+- Declarative
+- Text generation
 widget:
 - text: "q: do you have any hobbies that you like to do while you are at home ? a: watching online shows"
 - text: "q: do you watch a lot of comedy ? a: yes it will helpful the mind relaxation"
@@ -14,7 +20,9 @@ widget:
 Considering the question of "Which drug did you take?" and the answer of "Doliprane", the aim of this model is to derive a full answer to the question "I took Doliprane".
 
 ## Model training
-We fine-tune T5 (Raffel et al.,2019), a pre-trained encoder-decoder model, on two datasets of (question, incomplete answer, full answer) triples, one for wh- and one for yes-no(YN) questions. For wh-questions, we use 3,300 entries of the dataset consisting of (question, answer, declarative answer sentence) triples gathered by Demszky et al. (2018) using Amazon Mechanical Turk workers. For YN questions, we used the SAMSum corpus, (Gliwa et al., 2019) which contains short dialogs in chit-chat format. We created
+We fine-tune T5 (Raffel et al.,2019), a pre-trained encoder-decoder model, on two datasets of (question, incomplete answer, full answer) triples, one for wh- and one for yes-no(YN) questions. 
+For wh-questions, we use 3,300 entries of the dataset consisting of (question, answer, declarative answer sentence) triples gathered by Demszky et al. (2018) using Amazon Mechanical Turk workers. 
+For YN questions, we used the SAMSum corpus, (Gliwa et al., 2019) which contains short dialogs in chit-chat format. We created
 1,100 (question, answer, full answer) triples by au-
 tomatically extracting YN (question, answer) pairs
 from this corpus and manually associating them