Fix bugs in NewsQA dataset (#3734)

* Fix parsing of validated_answers

* Parse also badQuestion field

* Fix Features inside Features

* Refactor script

* Update metadata JSON

* Update dataset card

Commit from https://github.com/huggingface/datasets/commit/6103a627e57e13f4b7ec16fedacbcf7066b31dee

README.md

@@ -78,53 +78,156 @@ English
 ### Data Instances
 
 ```
-{'questions': {'answers': [{'sourcerAnswers': [{'e': [297], 'noAnswer': [False], 's': [294]}, {'e': [0], 'noAnswer': [True], 's': [0]}, {'e': [0], 'noAnswer': [True], 's': [0]}]}, {'sourcerAnswers': [{'e': [271], 'noAnswer': [False], 's': [261]}, {'e': [271], 'noAnswer': [False], 's': [258]}, {'e': [271], 'noAnswer': [False], 's': [261]}]}, {'sourcerAnswers': [{'e': [33], 'noAnswer': [False], 's': [26]}, {'e': [0], 'noAnswer': [True], 's': [0]}, {'e': [640], 'noAnswer': [False], 's': [624]}]}, {'sourcerAnswers': [{'e': [218], 'noAnswer': [False], 's': [195]}, {'e': [218], 'noAnswer': [False], 's': [195]}]}, {'sourcerAnswers': [{'e': [0], 'noAnswer': [True], 's': [0]}, {'e': [218, 271], 'noAnswer': [False, False], 's': [195, 232]}, {'e': [0], 'noAnswer': [True], 's': [0]}]}, {'sourcerAnswers': [{'e': [192], 'noAnswer': [False], 's': [129]}, {'e': [151], 'noAnswer': [False], 's': [129]}, {'e': [151], 'noAnswer': [False], 's': [133]}]}, {'sourcerAnswers': [{'e': [218], 'noAnswer': [False], 's': [195]}, {'e': [218], 'noAnswer': [False], 's': [195]}]}, {'sourcerAnswers': [{'e': [297], 'noAnswer': [False], 's': [294]}, {'e': [297], 'noAnswer': [False], 's': [294]}]}, {'sourcerAnswers': [{'e': [297], 'noAnswer': [False], 's': [294]}, {'e': [297], 'noAnswer': [False], 's': [294]}]}], 'consensus': [{'badQuestion': False, 'e': 297, 'noAnswer': False, 's': 294}, {'badQuestion': False, 'e': 271, 'noAnswer': False, 's': 261}, {'badQuestion': False, 'e': 640, 'noAnswer': False, 's': 624}, {'badQuestion': False, 'e': 218, 'noAnswer': False, 's': 195}, {'badQuestion': False, 'e': 218, 'noAnswer': False, 's': 195}, {'badQuestion': False, 'e': 151, 'noAnswer': False, 's': 129}, {'badQuestion': False, 'e': 218, 'noAnswer': False, 's': 195}, {'badQuestion': False, 'e': 297, 'noAnswer': False, 's': 294}, {'badQuestion': False, 'e': 297, 'noAnswer': False, 's': 294}], 'isAnswerAbsent': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'isQuestionBad': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'q': ['What was the amount of children murdered?', 'When was Pandher sentenced to death?', 'The court aquitted Moninder Singh Pandher of what crime?', 'who was acquitted', 'who was sentenced', 'What was Moninder Singh Pandher acquitted for?', 'Who was sentenced to death in February?', 'how many people died', 'How many children and young women were murdered?'], 'validated_answers': [{'sourcerAnswers': [{'count': [0], 'e': [297], 'noAnswer': [False], 's': [294]}, {'count': [0], 'e': [0], 'noAnswer': [True], 's': [0]}, {'count': [0], 'e': [0], 'noAnswer': [True], 's': [0]}]}, {'sourcerAnswers': [{'count': [0], 'e': [271], 'noAnswer': [False], 's': [261]}, {'count': [0], 'e': [271], 'noAnswer': [False], 's': [258]}, {'count': [0], 'e': [271], 'noAnswer': [False], 's': [261]}]}, {'sourcerAnswers': [{'count': [0], 'e': [33], 'noAnswer': [False], 's': [26]}, {'count': [0], 'e': [0], 'noAnswer': [True], 's': [0]}, {'count': [0], 'e': [640], 'noAnswer': [False], 's': [624]}]}, {'sourcerAnswers': [{'count': [0], 'e': [218], 'noAnswer': [False], 's': [195]}, {'count': [0], 'e': [218], 'noAnswer': [False], 's': [195]}]}, {'sourcerAnswers': [{'count': [0], 'e': [0], 'noAnswer': [True], 's': [0]}, {'count': [0, 0], 'e': [218, 271], 'noAnswer': [False, False], 's': [195, 232]}, {'count': [0], 'e': [0], 'noAnswer': [True], 's': [0]}]}, {'sourcerAnswers': [{'count': [0], 'e': [192], 'noAnswer': [False], 's': [129]}, {'count': [0], 'e': [151], 'noAnswer': [False], 's': [129]}, {'count': [0], 'e': [151], 'noAnswer': [False], 's': [133]}]}, {'sourcerAnswers': [{'count': [0], 'e': [218], 'noAnswer': [False], 's': [195]}, {'count': [0], 'e': [218], 'noAnswer': [False], 's': [195]}]}, {'sourcerAnswers': [{'count': [0], 'e': [297], 'noAnswer': [False], 's': [294]}, {'count': [0], 'e': [297], 'noAnswer': [False], 's': [294]}]}, {'sourcerAnswers': [{'count': [0], 'e': [297], 'noAnswer': [False], 's': [294]}, {'count': [0], 'e': [297], 'noAnswer': [False], 's': [294]}]}]}, 'storyId': './cnn/stories/42d01e187213e86f5fe617fe32e716ff7fa3afc4.story', 'text': 'NEW DELHI, India (CNN) -- A high court in northern India on Friday acquitted a wealthy businessman facing the death sentence for the killing of a teen in a case dubbed "the house of horrors."\n\n\n\nMoninder Singh Pandher was sentenced to death by a lower court in February.\n\n\n\nThe teen was one of 19 victims -- children and young women -- in one of the most gruesome serial killings in India in recent years.\n\n\n\nThe Allahabad high court has acquitted Moninder Singh Pandher, his lawyer Sikandar B. Kochar told CNN.\n\n\n\nPandher and his domestic employee Surinder Koli were sentenced to death in February by a lower court for the rape and murder of the 14-year-old.\n\n\n\nThe high court upheld Koli\'s death sentence, Kochar said.\n\n\n\nThe two were arrested two years ago after body parts packed in plastic bags were found near their home in Noida, a New Delhi suburb. Their home was later dubbed a "house of horrors" by the Indian media.\n\n\n\nPandher was not named a main suspect by investigators initially, but was summoned as co-accused during the trial, Kochar said.\n\n\n\nKochar said his client was in Australia when the teen was raped and killed.\n\n\n\nPandher faces trial in the remaining 18 killings and could remain in custody, the attorney said.', 'type': 'train'}
+{'storyId': './cnn/stories/42d01e187213e86f5fe617fe32e716ff7fa3afc4.story',
+ 'text': 'NEW DELHI, India (CNN) -- A high court in northern India on Friday acquitted a wealthy businessman facing the death sentence for the killing of a teen in a case dubbed "the house of horrors."\n\n\n\nMoninder Singh Pandher was sentenced to death by a lower court in February.\n\n\n\nThe teen was one of 19 victims -- children and young women -- in one of the most gruesome serial killings in India in recent years.\n\n\n\nThe Allahabad high court has acquitted Moninder Singh Pandher, his lawyer Sikandar B. Kochar told CNN.\n\n\n\nPandher and his domestic employee Surinder Koli were sentenced to death in February by a lower court for the rape and murder of the 14-year-old.\n\n\n\nThe high court upheld Koli\'s death sentence, Kochar said.\n\n\n\nThe two were arrested two years ago after body parts packed in plastic bags were found near their home in Noida, a New Delhi suburb. Their home was later dubbed a "house of horrors" by the Indian media.\n\n\n\nPandher was not named a main suspect by investigators initially, but was summoned as co-accused during the trial, Kochar said.\n\n\n\nKochar said his client was in Australia when the teen was raped and killed.\n\n\n\nPandher faces trial in the remaining 18 killings and could remain in custody, the attorney said.',
+ 'type': 'train',
+ 'questions': {'q': ['What was the amount of children murdered?',
+   'When was Pandher sentenced to death?',
+   'The court aquitted Moninder Singh Pandher of what crime?',
+   'who was acquitted',
+   'who was sentenced',
+   'What was Moninder Singh Pandher acquitted for?',
+   'Who was sentenced to death in February?',
+   'how many people died',
+   'How many children and young women were murdered?'],
+  'isAnswerAbsent': [0, 0, 0, 0, 0, 0, 0, 0, 0],
+  'isQuestionBad': [0, 0, 0, 0, 0, 0, 0, 0, 0],
+  'consensus': [{'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False},
+   {'s': 261, 'e': 271, 'badQuestion': False, 'noAnswer': False},
+   {'s': 624, 'e': 640, 'badQuestion': False, 'noAnswer': False},
+   {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False},
+   {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False},
+   {'s': 129, 'e': 151, 'badQuestion': False, 'noAnswer': False},
+   {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False},
+   {'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False},
+   {'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False}],
+  'answers': [{'sourcerAnswers': [{'s': [294],
+      'e': [297],
+      'badQuestion': [False],
+      'noAnswer': [False]},
+     {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]},
+     {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}]},
+   {'sourcerAnswers': [{'s': [261],
+      'e': [271],
+      'badQuestion': [False],
+      'noAnswer': [False]},
+     {'s': [258], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]},
+     {'s': [261], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]}]},
+   {'sourcerAnswers': [{'s': [26],
+      'e': [33],
+      'badQuestion': [False],
+      'noAnswer': [False]},
+     {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]},
+     {'s': [624], 'e': [640], 'badQuestion': [False], 'noAnswer': [False]}]},
+   {'sourcerAnswers': [{'s': [195],
+      'e': [218],
+      'badQuestion': [False],
+      'noAnswer': [False]},
+     {'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}]},
+   {'sourcerAnswers': [{'s': [0],
+      'e': [0],
+      'badQuestion': [False],
+      'noAnswer': [True]},
+     {'s': [195, 232],
+      'e': [218, 271],
+      'badQuestion': [False, False],
+      'noAnswer': [False, False]},
+     {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}]},
+   {'sourcerAnswers': [{'s': [129],
+      'e': [192],
+      'badQuestion': [False],
+      'noAnswer': [False]},
+     {'s': [129], 'e': [151], 'badQuestion': [False], 'noAnswer': [False]},
+     {'s': [133], 'e': [151], 'badQuestion': [False], 'noAnswer': [False]}]},
+   {'sourcerAnswers': [{'s': [195],
+      'e': [218],
+      'badQuestion': [False],
+      'noAnswer': [False]},
+     {'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}]},
+   {'sourcerAnswers': [{'s': [294],
+      'e': [297],
+      'badQuestion': [False],
+      'noAnswer': [False]},
+     {'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}]},
+   {'sourcerAnswers': [{'s': [294],
+      'e': [297],
+      'badQuestion': [False],
+      'noAnswer': [False]},
+     {'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}]}],
+  'validated_answers': [{'s': [0, 294],
+    'e': [0, 297],
+    'badQuestion': [False, False],
+    'noAnswer': [True, False],
+    'count': [1, 2]},
+   {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []},
+   {'s': [624],
+    'e': [640],
+    'badQuestion': [False],
+    'noAnswer': [False],
+    'count': [2]},
+   {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []},
+   {'s': [195],
+    'e': [218],
+    'badQuestion': [False],
+    'noAnswer': [False],
+    'count': [2]},
+   {'s': [129],
+    'e': [151],
+    'badQuestion': [False],
+    'noAnswer': [False],
+    'count': [2]},
+   {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []},
+   {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []},
+   {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}]}}
 ```
 
 ### Data Fields
 
-
 Configuration: combined-csv
- - 'story_id': An identifier of the story
-- 'story_text': text of the story
+- 'story_id': An identifier of the story.
+- 'story_text': Text of the story.
 - 'question': A question about the story.
-- 'answer_char_ranges': The raw data collected for character based indices to answers in story_text. E.g. 196:228|196:202,217:228|None. Answers from different crowdsourcers are separated by |, within those, multiple selections from the same crowdsourcer are separated by ,. None means the crowdsourcer thought there was no answer to the question in the story. The start is inclusive and the end is exclusive. The end may point to whitespace after a token.
+- 'answer_char_ranges': The raw data collected for character based indices to answers in story_text. E.g. 196:228|196:202,217:228|None. Answers from different crowdsourcers are separated by `|`; within those, multiple selections from the same crowdsourcer are separated by `,`. `None` means the crowdsourcer thought there was no answer to the question in the story. The start is inclusive and the end is exclusive. The end may point to whitespace after a token.
 
-Configuration: combined-csv
+Configuration: combined-json
 - 'storyId': An identifier of the story.
-- 'text': Text of the story
-- 'type': Split type - train, validation or test
-- 'questions': A list containing the following. 
-  - 'q': A question
+- 'text': Text of the story.
+- 'type': Split type. Will be "train", "validation" or "test".
+- 'questions': A list containing the following: 
+  - 'q': A question about the story.
   - 'isAnswerAbsent': Proportion of crowdsourcers that said there was no answer to the question in the story.
   - 'isQuestionBad': Proportion of crowdsourcers that said the question does not make sense.
-  - 'consensus': The consensus answer. Use this field to pick the best continuous answer span from the text. If you want to know about a question having multiple answers in the text then you can use the more detailed "answers" and "validatedAnswers". The object can have start and end positions like in the example above or can be {"badQuestion": true} or {"noAnswer": true}. Note that there is only one consensus answer since it's based on the majority agreement of the crowdsourcers.
-  - 's': start of the answer
-  - 'e': end of the answer
+  - 'consensus': The consensus answer. Use this field to pick the best continuous answer span from the text. If you want to know about a question having multiple answers in the text then you can use the more detailed "answers" and "validated_answers". The object can have start and end positions like in the example above or can be {"badQuestion": true} or {"noAnswer": true}. Note that there is only one consensus answer since it's based on the majority agreement of the crowdsourcers.
+  - 's': Start of the answer. The first character of the answer in "text" (inclusive).
+  - 'e': End of the answer. The last character of the answer in "text" (exclusive).
   - 'badQuestion': The validator said that the question did not make sense.
   - 'noAnswer': The crowdsourcer said that there was no answer to the question in the text.
   - 'answers': The answers from various crowdsourcers.
     - 'sourcerAnswers': The answer provided from one crowdsourcer.
-      - 's': start
-      - 'e': end
+      - 's': Start of the answer. The first character of the answer in "text" (inclusive).
+      - 'e': End of the answer. The last character of the answer in "text" (exclusive).
+      - 'badQuestion': The crowdsourcer said that the question did not make sense.
       - 'noAnswer': The crowdsourcer said that there was no answer to the question in the text.
   - 'validated_answers': The answers from the validators.
-    - 'sourcerAnswers': The answer provided from one crowdsourcer.
-      - 's': start
-      - 'e': end
-      - 'noAnswer': The crowdsourcer said that there was no answer to the question in the text.
-      - 'count': The number of validators that agreed with this answer.
+    - 's': Start of the answer. The first character of the answer in "text" (inclusive).
+    - 'e': End of the answer. The last character of the answer in "text" (exclusive).
+    - 'badQuestion': The validator said that the question did not make sense.
+    - 'noAnswer': The validator said that there was no answer to the question in the text.
+    - 'count': The number of validators that agreed with this answer.
 
 Configuration: split
- - 'story_id': An identifier of the story
+- 'story_id': An identifier of the story
 - 'story_text': text of the story
 - 'question': A question about the story.
-- 'answer_token_ranges': Word based indices to answers in story_text. E.g. 196:202,217:228. Multiple selections from the same answer are separated by ,. The start is inclusive and the end is exclusive. The end may point to whitespace after a token.
+- 'answer_token_ranges': Word based indices to answers in story_text. E.g. 196:202,217:228. Multiple selections from the same answer are separated by `,`. The start is inclusive and the end is exclusive. The end may point to whitespace after a token.
 
 ### Data Splits
 
-split: Train, Validation and Test.
-combined-csv and combined-json: train (whole dataset)
+| name          |      train | validation |    test |
+|---------------|-----------:|-----------:|--------:|
+| combined-csv  |     119633 |            |         |
+| combined-json |      12744 |            |         |
+| split         |      92549 |       5166 |    5126 |
 
 ## Dataset Creation
 

dataset_infos.json

@@ -1 +1 @@
-{"combined-csv": {"description": "NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles.\n", "citation": "@inproceedings{trischler2017newsqa,\n  title={NewsQA: A Machine Comprehension Dataset},\n  author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},\n  booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},\n  pages={191--200},\n  year={2017}\n}\n\n", "homepage": "https://www.microsoft.com/en-us/research/project/newsqa-dataset/", "license": "NewsQA CodeCopyright (c) Microsoft CorporationAll rights reserved.MIT LicensePermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\u00a9 2020 GitHub, Inc.", "features": {"story_id": {"dtype": "string", "id": null, "_type": "Value"}, "story_text": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer_char_ranges": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "newsqa", "config_name": "combined-csv", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 465942194, "num_examples": 119633, "dataset_name": "newsqa"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 465942194, "size_in_bytes": 465942194}, "combined-json": {"description": "NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles.\n", "citation": "@inproceedings{trischler2017newsqa,\n  title={NewsQA: A Machine Comprehension Dataset},\n  author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},\n  booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},\n  pages={191--200},\n  year={2017}\n}\n\n", "homepage": "https://www.microsoft.com/en-us/research/project/newsqa-dataset/", "license": "NewsQA CodeCopyright (c) Microsoft CorporationAll rights reserved.MIT LicensePermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\u00a9 2020 GitHub, Inc.", "features": {"storyId": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"q": {"dtype": "string", "id": null, "_type": "Value"}, "isAnswerAbsent": {"dtype": "int32", "id": null, "_type": "Value"}, "isQuestionBad": {"dtype": "int32", "id": null, "_type": "Value"}, "consensus": {"s": {"dtype": "int32", "id": null, "_type": "Value"}, "e": {"dtype": "int32", "id": null, "_type": "Value"}, "badQuestion": {"dtype": "bool", "id": null, "_type": "Value"}, "noAnswer": {"dtype": "bool", "id": null, "_type": "Value"}}, "answers": {"feature": {"sourcerAnswers": {"feature": {"s": {"dtype": "int32", "id": null, "_type": "Value"}, "e": {"dtype": "int32", "id": null, "_type": "Value"}, "noAnswer": {"dtype": "bool", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "validated_answers": {"feature": {"sourcerAnswers": {"feature": {"s": {"dtype": "int32", "id": null, "_type": "Value"}, "e": {"dtype": "int32", "id": null, "_type": "Value"}, "noAnswer": {"dtype": "bool", "id": null, "_type": "Value"}, "count": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "newsqa", "config_name": "combined-json", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 74492925, "num_examples": 12744, "dataset_name": "newsqa"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 74492925, "size_in_bytes": 74492925}, "split": {"description": "NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles.\n", "citation": "@inproceedings{trischler2017newsqa,\n  title={NewsQA: A Machine Comprehension Dataset},\n  author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},\n  booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},\n  pages={191--200},\n  year={2017}\n}\n\n", "homepage": "https://www.microsoft.com/en-us/research/project/newsqa-dataset/", "license": "NewsQA CodeCopyright (c) Microsoft CorporationAll rights reserved.MIT LicensePermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\u00a9 2020 GitHub, Inc.", "features": {"story_id": {"dtype": "string", "id": null, "_type": "Value"}, "story_text": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer_token_ranges": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "newsqa", "config_name": "split", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 362031288, "num_examples": 92549, "dataset_name": "newsqa"}, "test": {"name": "test", "num_bytes": 19763673, "num_examples": 5126, "dataset_name": "newsqa"}, "validation": {"name": "validation", "num_bytes": 19862778, "num_examples": 5166, "dataset_name": "newsqa"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 401657739, "size_in_bytes": 401657739}}
+{"combined-csv": {"description": "NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles.\n", "citation": "@inproceedings{trischler2017newsqa,\n  title={NewsQA: A Machine Comprehension Dataset},\n  author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},\n  booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},\n  pages={191--200},\n  year={2017}\n}\n\n", "homepage": "https://www.microsoft.com/en-us/research/project/newsqa-dataset/", "license": "NewsQA CodeCopyright (c) Microsoft CorporationAll rights reserved.MIT LicensePermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\u00a9 2020 GitHub, Inc.", "features": {"story_id": {"dtype": "string", "id": null, "_type": "Value"}, "story_text": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer_char_ranges": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "newsqa", "config_name": "combined-csv", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 465942194, "num_examples": 119633, "dataset_name": "newsqa"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 465942194, "size_in_bytes": 465942194}, "combined-json": {"description": "NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles.\n", "citation": "@inproceedings{trischler2017newsqa,\n  title={NewsQA: A Machine Comprehension Dataset},\n  author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},\n  booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},\n  pages={191--200},\n  year={2017}\n}\n\n", "homepage": "https://www.microsoft.com/en-us/research/project/newsqa-dataset/", "license": "NewsQA CodeCopyright (c) Microsoft CorporationAll rights reserved.MIT LicensePermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\u00a9 2020 GitHub, Inc.", "features": {"storyId": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"q": {"dtype": "string", "id": null, "_type": "Value"}, "isAnswerAbsent": {"dtype": "int32", "id": null, "_type": "Value"}, "isQuestionBad": {"dtype": "int32", "id": null, "_type": "Value"}, "consensus": {"s": {"dtype": "int32", "id": null, "_type": "Value"}, "e": {"dtype": "int32", "id": null, "_type": "Value"}, "badQuestion": {"dtype": "bool", "id": null, "_type": "Value"}, "noAnswer": {"dtype": "bool", "id": null, "_type": "Value"}}, "answers": {"feature": {"sourcerAnswers": {"feature": {"s": {"dtype": "int32", "id": null, "_type": "Value"}, "e": {"dtype": "int32", "id": null, "_type": "Value"}, "badQuestion": {"dtype": "bool", "id": null, "_type": "Value"}, "noAnswer": {"dtype": "bool", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "validated_answers": {"feature": {"s": {"dtype": "int32", "id": null, "_type": "Value"}, "e": {"dtype": "int32", "id": null, "_type": "Value"}, "badQuestion": {"dtype": "bool", "id": null, "_type": "Value"}, "noAnswer": {"dtype": "bool", "id": null, "_type": "Value"}, "count": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "newsqa", "config_name": "combined-json", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 68667276, "num_examples": 12744, "dataset_name": "newsqa"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 68667276, "size_in_bytes": 68667276}, "split": {"description": "NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles.\n", "citation": "@inproceedings{trischler2017newsqa,\n  title={NewsQA: A Machine Comprehension Dataset},\n  author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},\n  booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},\n  pages={191--200},\n  year={2017}\n}\n\n", "homepage": "https://www.microsoft.com/en-us/research/project/newsqa-dataset/", "license": "NewsQA CodeCopyright (c) Microsoft CorporationAll rights reserved.MIT LicensePermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\u00a9 2020 GitHub, Inc.", "features": {"story_id": {"dtype": "string", "id": null, "_type": "Value"}, "story_text": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer_token_ranges": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "newsqa", "config_name": "split", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 362031288, "num_examples": 92549, "dataset_name": "newsqa"}, "test": {"name": "test", "num_bytes": 19763673, "num_examples": 5126, "dataset_name": "newsqa"}, "validation": {"name": "validation", "num_bytes": 19862778, "num_examples": 5166, "dataset_name": "newsqa"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 401657739, "size_in_bytes": 401657739}}

newsqa.py

@@ -18,6 +18,7 @@
 import csv
 import json
 import os
+from textwrap import dedent
 
 import datasets
 
@@ -78,19 +79,17 @@ class Newsqa(datasets.GeneratorBasedBuilder):
 
     @property
     def manual_download_instructions(self):
-        return """ Due to legal restrictions with the CNN data and data extraction. The data has to be downloaded from several sources and compiled as per the instructions by Authors. \
-        Upon obtaining the resulting data folders, it can be loaded easily using the datasets API. \
-        Please refer to (https://github.com/Maluuba/newsqa) to download data from Microsoft Reseach site (https://msropendata.com/datasets/939b1042-6402-4697-9c15-7a28de7e1321) \
-        and a CNN datasource (https://cs.nyu.edu/~kcho/DMQA/) and run the scripts present here (https://github.com/Maluuba/newsqa).\
-        This will generate a folder named "split-data" and a file named "combined-newsqa-data-v1.csv".\
-        Copy the above folder and the file to a directory where you want to store them locally.\
-        They must be used to load the dataset via `datasets.load_dataset("narqa", data_dir="<path/to/folder>")."""
+        return dedent(
+            """\
+            Due to legal restrictions with the CNN data and data extraction. The data has to be downloaded from several sources and compiled as per the instructions by Authors.
+            Upon obtaining the resulting data folders, it can be loaded easily using the datasets API.
+            Please refer to (https://github.com/Maluuba/newsqa) to download data from Microsoft Reseach site (https://msropendata.com/datasets/939b1042-6402-4697-9c15-7a28de7e1321) and a CNN datasource (https://cs.nyu.edu/~kcho/DMQA/) and run the scripts present here (https://github.com/Maluuba/newsqa).
+            This will generate a folder named "split-data" and a file named "combined-newsqa-data-v1.csv".
+            Copy the above folder and the file to a directory where you want to store them locally."""
+        )
 
     def _info(self):
-
-        if (
-            self.config.name == "combined-csv"
-        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        if self.config.name == "combined-csv":
             features = datasets.Features(
                 {
                     "story_id": datasets.Value("string"),
@@ -99,9 +98,7 @@ class Newsqa(datasets.GeneratorBasedBuilder):
                     "answer_char_ranges": datasets.Value("string"),
                 }
             )
-        elif (
-            self.config.name == "combined-json"
-        ):  # This is an example to show how to have different features for "first_domain" and "second_domain"
+        elif self.config.name == "combined-json":
             features = datasets.Features(
                 {
                     "storyId": datasets.Value("string"),
@@ -112,20 +109,19 @@ class Newsqa(datasets.GeneratorBasedBuilder):
                             "q": datasets.Value("string"),
                             "isAnswerAbsent": datasets.Value("int32"),
                             "isQuestionBad": datasets.Value("int32"),
-                            "consensus": datasets.Features(
-                                {
-                                    "s": datasets.Value("int32"),
-                                    "e": datasets.Value("int32"),
-                                    "badQuestion": datasets.Value("bool"),
-                                    "noAnswer": datasets.Value("bool"),
-                                }
-                            ),
+                            "consensus": {
+                                "s": datasets.Value("int32"),
+                                "e": datasets.Value("int32"),
+                                "badQuestion": datasets.Value("bool"),
+                                "noAnswer": datasets.Value("bool"),
+                            },
                             "answers": datasets.features.Sequence(
                                 {
                                     "sourcerAnswers": datasets.features.Sequence(
                                         {
                                             "s": datasets.Value("int32"),
                                             "e": datasets.Value("int32"),
+                                            "badQuestion": datasets.Value("bool"),
                                             "noAnswer": datasets.Value("bool"),
                                         }
                                     ),
@@ -133,21 +129,18 @@ class Newsqa(datasets.GeneratorBasedBuilder):
                             ),
                             "validated_answers": datasets.features.Sequence(
                                 {
-                                    "sourcerAnswers": datasets.features.Sequence(
-                                        {
-                                            "s": datasets.Value("int32"),
-                                            "e": datasets.Value("int32"),
-                                            "noAnswer": datasets.Value("bool"),
-                                            "count": datasets.Value("int32"),
-                                        }
-                                    ),
+                                    "s": datasets.Value("int32"),
+                                    "e": datasets.Value("int32"),
+                                    "badQuestion": datasets.Value("bool"),
+                                    "noAnswer": datasets.Value("bool"),
+                                    "count": datasets.Value("int32"),
                                 }
                             ),
                         }
                     ),
                 }
             )
-        else:  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        else:
             features = datasets.Features(
                 {
                     "story_id": datasets.Value("string"),
@@ -156,20 +149,12 @@ class Newsqa(datasets.GeneratorBasedBuilder):
                     "answer_token_ranges": datasets.Value("string"),
                 }
             )
+
         return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
+            features=features,
             homepage=_HOMEPAGE,
-            # License for the dataset if available
             license=_LICENSE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
@@ -177,10 +162,6 @@ class Newsqa(datasets.GeneratorBasedBuilder):
         """Returns SplitGenerators."""
 
         path_to_manual_folder = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
-        combined_file_csv = os.path.join(path_to_manual_folder, "combined-newsqa-data-v1.csv")
-        combined_file_json = os.path.join(path_to_manual_folder, "combined-newsqa-data-v1.json")
-        split_files = os.path.join(path_to_manual_folder, "split_data")
-
         if not os.path.exists(path_to_manual_folder):
             raise FileNotFoundError(
                 f"{path_to_manual_folder} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('newsqa', data_dir=...)` that includes files from the Manual download instructions: {self.manual_download_instructions}"
@@ -190,9 +171,8 @@ class Newsqa(datasets.GeneratorBasedBuilder):
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "filepath": combined_file_csv,
+                        "filepath": os.path.join(path_to_manual_folder, "combined-newsqa-data-v1.csv"),
                         "split": "combined",
                     },
                 )
@@ -201,18 +181,17 @@ class Newsqa(datasets.GeneratorBasedBuilder):
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "filepath": combined_file_json,
+                        "filepath": os.path.join(path_to_manual_folder, "combined-newsqa-data-v1.json"),
                         "split": "combined",
                     },
                 )
             ]
         else:
+            split_files = os.path.join(path_to_manual_folder, "split_data")
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
                     gen_kwargs={
                         "filepath": os.path.join(split_files, "train.csv"),
                         "split": "train",
@@ -220,12 +199,10 @@ class Newsqa(datasets.GeneratorBasedBuilder):
                 ),
                 datasets.SplitGenerator(
                     name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
                     gen_kwargs={"filepath": os.path.join(split_files, "test.csv"), "split": "test"},
                 ),
                 datasets.SplitGenerator(
                     name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
                     gen_kwargs={
                         "filepath": os.path.join(split_files, "dev.csv"),
                         "split": "dev",
@@ -255,61 +232,52 @@ class Newsqa(datasets.GeneratorBasedBuilder):
             with open(filepath, encoding="utf-8") as f:
                 d = json.load(f)
                 data = d["data"]
-
-                for id_, iter in enumerate(data):
-
+                for id_, item in enumerate(data):
+                    # questions
                     questions = []
-
-                    for ques in iter["questions"]:
-                        dict1 = {}
-                        dict1["q"] = ques["q"]
+                    for ques in item["questions"]:
+                        question = {"q": ques["q"]}
                         if "isAnswerAbsent" in ques.keys():
-                            dict1["isAnswerAbsent"] = ques["isAnswerAbsent"]
+                            question["isAnswerAbsent"] = ques["isAnswerAbsent"]
                         else:
-                            dict1["isAnswerAbsent"] = 0.0
+                            question["isAnswerAbsent"] = 0.0
                         if "isQuestionBad" in ques.keys():
-                            dict1["isQuestionBad"] = ques["isQuestionBad"]
+                            question["isQuestionBad"] = ques["isQuestionBad"]
                         else:
-                            dict1["isQuestionBad"] = 0.0
-                        dict1["consensus"] = {"s": 0, "e": 0, "badQuestion": False, "noAnswer": False}
-
-                        for keys in ques["consensus"]:
-                            dict1["consensus"][keys] = ques["consensus"][keys]
-
+                            question["isQuestionBad"] = 0.0
+                        question["consensus"] = {"s": 0, "e": 0, "badQuestion": False, "noAnswer": False}
+                        # consensus
+                        for key in ques["consensus"]:
+                            question["consensus"][key] = ques["consensus"][key]
+                        # answers
                         answers = []
                         for ans in ques["answers"]:
-                            dict2 = {}
-                            dict2["sourcerAnswers"] = []
-                            for index, i in enumerate(ans["sourcerAnswers"]):
-                                dict_temp = {"s": 0, "e": 0, "noAnswer": False}
-                                for keys in i.keys():
-                                    dict_temp[keys] = i[keys]
-                                dict2["sourcerAnswers"].append(dict_temp)
-
-                            answers.append(dict2)
-
-                        dict1["answers"] = answers
-
-                        validated_answers = []
-                        for ans in ques["answers"]:
-                            dict2 = {}
-                            dict2["sourcerAnswers"] = []
-                            for index, i in enumerate(ans["sourcerAnswers"]):
-                                dict_temp = {"s": 0, "e": 0, "noAnswer": False, "count": 0}
-                                for keys in i.keys():
-                                    dict_temp[keys] = i[keys]
-
-                                dict2["sourcerAnswers"].append(dict_temp)
-
-                            validated_answers.append(dict2)
+                            answer = {"sourcerAnswers": []}
+                            for sourcer_answer in ans["sourcerAnswers"]:
+                                dict_temp = {"s": 0, "e": 0, "badQuestion": False, "noAnswer": False}
+                                for key in sourcer_answer.keys():
+                                    dict_temp[key] = sourcer_answer[key]
+                                answer["sourcerAnswers"].append(dict_temp)
+                            answers.append(answer)
+                        question["answers"] = answers
+                        # validated_answers
+                        default_validated_answer = {
+                            "s": 0,
+                            "e": 0,
+                            "badQuestion": False,
+                            "noAnswer": False,
+                            "count": 0,
+                        }
+                        validated_answers = ques.get("validatedAnswers", [])  # not always present
+                        validated_answers = [{**default_validated_answer, **val_ans} for val_ans in validated_answers]
+                        question["validated_answers"] = validated_answers
 
-                        dict1["validated_answers"] = validated_answers
-                        questions.append(dict1)
+                        questions.append(question)
 
                     yield id_, {
-                        "storyId": iter["storyId"],
-                        "text": iter["text"],
-                        "type": iter["type"],
+                        "storyId": item["storyId"],
+                        "text": item["text"],
+                        "type": item["type"],
                         "questions": questions,
                     }
         else: