Update README.md

README.md

@@ -17,7 +17,11 @@ pretty_name: SynTranFa
 ---
 
 # SynTran-fa 
-Syntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers.
+Syntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers. You can use this dataset by the code below:
+```
+import datasets
+data = datasets.load_dataset('SLPL/syntran-fa', split="train")
+```
 
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
@@ -49,16 +53,16 @@ Syntactic Transformed Version of Farsi QA datasets to make fluent responses from
 - **Homepage:** [Sharif-SLPL](https://github.com/Sharif-SLPL)
 - **Repository:** [SynTran-fa](https://github.com/agp-internship/syntran-fa)
 - **Point of Contact:** [Sadra Sabouri](mailto:[email protected])
-- **Size of dataset files:** 6.12 MB
+- **Size of dataset files:** 6.68MB
 
 ### Dataset Summary
 
 Generating fluent responses has always been challenging for the question-answering task, especially in low-resource languages like Farsi. In recent years there were some efforts for enhancing the size of datasets in Farsi. Syntran-fa is a question-answering dataset that accumulates the former Farsi QA dataset's short answers and proposes a complete fluent answer for each pair of (question, short_answer).
 
 This dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources were as follows:
-+ QA_DATASET1
-+ QA_DATASET2
-+ ...
++ [PersianQA](https://github.com/sajjjadayobi/PersianQA)
++ [PersianQuAD](https://ieeexplore.ieee.org/document/9729745)
++ [PQuAD](https://arxiv.org/abs/2202.06219)
 
 The main idea for this dataset comes from [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf) where they used a "parser + syntactic rules" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used [stanza](https://stanfordnlp.github.io/stanza/) as our parser to parse the question and generate a response according to it using the short (1-2 word) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).
 
@@ -71,6 +75,17 @@ This dataset can be used for the question-answering task, especially when you ar
 + Persian (fa)
 
 ## Dataset Structure
+Each row of the dataset will look like something like the below:
+```
+{
+  'id': 0,
+  'question': 'باشگاه هاکی ساوتهمپتون چه نام دارد؟',
+  'short_answer': 'باشگاه هاکی ساوتهمپتون',
+  'fluent_answer': 'باشگاه هاکی ساوتهمپتون باشگاه هاکی ساوتهمپتون نام دارد.',
+  'bert_loss': 1.110097069682014
+}
+```
++ __id__: 
 
 ### Data Instances