Update README.md

README.md

@@ -1,39 +1,140 @@
 ---
+pretty_name: PQuAD
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- fa
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+- extractive-qa
+paperswithcode_id: squad
+train-eval-index:
+- config: pquad
+  task: question-answering
+  task_id: extractive_question_answering
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    question: question
+    context: context
+    answers:
+      text: text
+      answer_start: answer_start
+  metrics:
+  - type: pquad
+    name: PQuAD
 dataset_info:
   features:
+  - name: id
+    dtype: int32
   - name: title
     dtype: string
-  - name: paragraphs
-    list:
-    - name: context
+  - name: context
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answers
+    sequence:
+    - name: text
       dtype: string
-    - name: qas
-      list:
-      - name: answers
-        list:
-        - name: answer_start
-          dtype: int64
-        - name: text
-          dtype: string
-      - name: id
-        dtype: string
-      - name: is_impossible
-        dtype: bool
-      - name: question
-        dtype: string
+    - name: answer_start
+      dtype: int32
+  config_name: pquad
   splits:
   - name: train
-    num_bytes: 21744137
-    num_examples: 891
+    num_bytes: ...
+    num_examples: 63994
+  - name: validation
+    num_bytes: ...
+    num_examples: 7976
   - name: test
-    num_bytes: 2810299
-    num_examples: 114
-  - name: Validation
-    num_bytes: 2850767
-    num_examples: 120
-  download_size: 12488927
-  dataset_size: 27405203
+    num_bytes: ...
+    num_examples: 8002
+  download_size: ...
+  dataset_size: ...
 ---
+
 # Dataset Card for "pquad"
+## PQuAD
+
+*THIS IS A NON-OFFICIAL VERSION OF THE DATASET UPLOADED TO HUGGINGFACE BY [Gholamreza Dar](https://huggingface.co/Gholamreza)*
+The original repository for the dataset is https://github.com/AUT-NLP/PQuAD
+
+Original README.md:
+
+
+PQuAD is a crowd- sourced reading comprehension dataset on Persian Language. It includes 80,000 
+questions along with their answers, with 25% of the questions being unanswerable. As a reading 
+comprehension dataset, it requires a system to read a passage and then answer the given questions 
+from the passage. PQuAD's questions are based on Persian Wikipedia articles and cover a wide 
+variety of subjects. Articles used for question generation are quality checked and include few 
+number of non-Persian words. 
+
+The dataset is divided into three categories including train, validation, and test sets and the 
+statistics of these sets are as follows:
+
+```
++----------------------------+-------+------------+------+-------+
+|                            | Train | Validation | Test | Total |
++----------------------------+-------+------------+------+-------+
+|      Total Questions       | 63994 |    7976    | 8002 | 79972 |
+|   Unanswerable Questions   | 15721 |    1981    | 1914 | 19616 |
+| Mean # of paragraph tokens |  125  |    121     | 124  |  125  |
+| Mean # of question tokens  |   10  |     11     |  11  |   10  |
+|  Mean # of answer tokens   |   5   |     6      |  5   |   5   |
++----------------------------+-------+------------+------+-------+
+```
+
+Workers were encouraged to use paraphrased sentences in their questions and avoid choosing the 
+answers comprising non-Persian words. Another group of crowdworkers validated the questions and 
+answers in the test and validation set to ensure their quality. They also provided additional 
+answers to the questions in test and validation sets if possible. This helps to consider all 
+possible types of answers and have a better evaluation of models.
+
+PQuAD is stored in the JSON format and consists of passages where each passage is linked to a 
+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.
+
+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 
+al., 2017) which is an attention-based model proposed for MRC.
+
+```
++-------------+------+------+-----------+-----------+-------------+
+|    Model    |  EM  |  F1  | HasAns_EM | HasAns_F1 | NoAns_EM/F1 |
++-------------+------+------+-----------+-----------+-------------+
+|     BNA     | 54.4 | 71.4 |    43.9   |    66.4   |     87.6    |
+|   ParsBERT  | 68.1 | 82.0 |    61.5   |    79.8   |     89.0    |
+| XLM-RoBERTa | 74.8 | 87.6 |    69.1   |    86.0   |     92.7    |
+|    Human    | 80.3 | 88.3 |    74.9   |    85.6   |     96.8    |
++-------------+------+------+-----------+-----------+-------------+
+```
+
+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 
+on Persian reading comprehension and the development of Persian question answering systems.
+
+This work is licensed under a
+[Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa].
+
+[![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa]
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+[cc-by-sa]: http://creativecommons.org/licenses/by-sa/4.0/
+[cc-by-sa-image]: https://licensebuttons.net/l/by-sa/4.0/88x31.png
+[cc-by-sa-shield]: https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg
+# Dataset Card for "pquad"