Datasets:

Gholamreza
/

pquad

@@ -108,7 +108,7 @@ PQuAD is stored in the JSON format and consists of passages where each passage i
 set of questions. Answer(s) of the questions is specified with answer's span (start and end
 point of answer in paragraph). Also, the unanswerable questions are marked as unanswerable.
-##Results
 The estimated human performance on the test set is 88.3% for F1 and 80.3% for EM. We have
 evaluated PQuAD using two pre-trained transformer-based language models, namely ParsBERT
 (Farahani et al., 2021) and XLM-RoBERTa (Conneau et al., 2020), as well as BiDAF (Levy et
@@ -125,7 +125,7 @@ al., 2017) which is an attention-based model proposed for MRC.
 +-------------+------+------+-----------+-----------+-------------+
 ```
-##LICENSE
 PQuAD is developed by Mabna Intelligent Computing at Amirkabir Science and Technology Park with
 collaboration of the NLP lab of the Amirkabir University of Technology and is supported by the
 Vice Presidency for Scientific and Technology. By releasing this dataset, we aim to ease research

 set of questions. Answer(s) of the questions is specified with answer's span (start and end
 point of answer in paragraph). Also, the unanswerable questions are marked as unanswerable.
+## Results
 The estimated human performance on the test set is 88.3% for F1 and 80.3% for EM. We have
 evaluated PQuAD using two pre-trained transformer-based language models, namely ParsBERT
 (Farahani et al., 2021) and XLM-RoBERTa (Conneau et al., 2020), as well as BiDAF (Levy et
 +-------------+------+------+-----------+-----------+-------------+
 ```
+## LICENSE
 PQuAD is developed by Mabna Intelligent Computing at Amirkabir Science and Technology Park with
 collaboration of the NLP lab of the Amirkabir University of Technology and is supported by the
 Vice Presidency for Scientific and Technology. By releasing this dataset, we aim to ease research