upload

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "The FairytaleQA dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. This is for the Question Generation Task of FairytaleQA.\n", "citation": "@inproceedings{xu2022fairytaleqa,\n    author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and Yu, Zhou and Warschauer, Mark},\n    title = {Fantastic Questions and Where to Find Them: Fairytale{QA} -- An Authentic Dataset for Narrative Comprehension},\n    publisher = {Association for Computational Linguistics},\n    year = {2022}\n}\n", "homepage": "https://github.com/uci-soe/FairytaleQAData", "license": "", "features": {"story_name": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "gem_id": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}, "references": [{"dtype": "string", "id": null, "_type": "Value"}], "local_or_sum": {"dtype": "string", "id": null, "_type": "Value"}, "attribute": {"dtype": "string", "id": null, "_type": "Value"}, "ex_or_im": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fairytale_qa", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10644996, "num_examples": 8548, "dataset_name": "fairytale_qa"}, "validation": {"name": "validation", "num_bytes": 1266906, "num_examples": 1025, "dataset_name": "fairytale_qa"}, "test": {"name": "test", "num_bytes": 1230405, "num_examples": 1007, "dataset_name": "fairytale_qa"}}, "download_checksums": {"train.json": {"num_bytes": 11603740, "checksum": "0fa291ddf323a73f5210d5c48c56ed8aa348d9a1ba8d9917c4fc2d72b484cc30"}, "valid.json": {"num_bytes": 1314327, "checksum": "963d2115e4fecddb9572cc18d0a2c82d483e6ab565e4835b5b5ed51eb6cabd0e"}, "test.json": {"num_bytes": 1285095, "checksum": "dd1e663069786ebd24f5325aadd00bab73a289692c8375488281c5a7d6d555c6"}}, "download_size": 14203162, "post_processing_size": null, "dataset_size": 13142307, "size_in_bytes": 27345469}}
+{"default": {"description": "The FairytaleQA dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. This is for the Question Generation Task of FairytaleQA.\n", "citation": "@inproceedings{xu2022fairytaleqa,\n    author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and Yu, Zhou and Warschauer, Mark},\n    title = {Fantastic Questions and Where to Find Them: Fairytale{QA} -- An Authentic Dataset for Narrative Comprehension},\n    publisher = {Association for Computational Linguistics},\n    year = {2022}\n}\n", "homepage": "https://github.com/uci-soe/FairytaleQAData", "license": "", "features": {"story_name": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "gem_id": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}, "references": [{"dtype": "string", "id": null, "_type": "Value"}], "local_or_sum": {"dtype": "string", "id": null, "_type": "Value"}, "attribute": {"dtype": "string", "id": null, "_type": "Value"}, "ex_or_im": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "fairytale_qa", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10644996, "num_examples": 8548, "dataset_name": "fairytale_qa"}, "validation": {"name": "validation", "num_bytes": 1266906, "num_examples": 1025, "dataset_name": "fairytale_qa"}, "test": {"name": "test", "num_bytes": 1230405, "num_examples": 1007, "dataset_name": "fairytale_qa"}}, "download_checksums": {"train.json": {"num_bytes": 11150678, "checksum": "41c17f4b51f52b215af05fdaee66f7f31de24455083c395837565b91f35b50c1"}, "valid.json": {"num_bytes": 1259984, "checksum": "d14f070e6b11334d054efeb795574833450a41a8d6403c201e1a8cafc2733fd4"}, "test.json": {"num_bytes": 1231706, "checksum": "8e5fcacea2da0cce61a151074adaf45e6080fd6670be84c44a66eb02388b9e08"}}, "download_size": 13642368, "post_processing_size": null, "dataset_size": 13142307, "size_in_bytes": 26784675}}

fairytaleqa.py

@@ -155,17 +155,18 @@ class FairytaleQA(datasets.GeneratorBasedBuilder):
 
         with open(filepath, encoding="utf-8") as f:
 
-            json_data = json.load(f)
+            # json_data = json.load(f)
             
             
-            for id_, row in enumerate(json_data["data"]): 
-                story_name = row['story_name']
-                content = row['content']
-                answer = row['answer']
-                question = row['question']
-                local_or_sum = row['local_or_sum']
-                attribute = row['attribute']
-                ex_or_im = row['ex_or_im']
+            for id_, row in enumerate(f): 
+                data = json.loads(row)
+                story_name = data['story_name']
+                content = data['content']
+                answer = data['answer']
+                question = data['question']
+                local_or_sum = data['local_or_sum']
+                attribute = data['attribute']
+                ex_or_im = data['ex_or_im']
                 # data = json.loads(row)
 
                 yield id_, {

test.json

@@ -1,3 +1,3 @@
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-oid sha256:dd1e663069786ebd24f5325aadd00bab73a289692c8375488281c5a7d6d555c6
-size 1285095
+oid sha256:8e5fcacea2da0cce61a151074adaf45e6080fd6670be84c44a66eb02388b9e08
+size 1231706

train.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0fa291ddf323a73f5210d5c48c56ed8aa348d9a1ba8d9917c4fc2d72b484cc30
-size 11603740
+oid sha256:41c17f4b51f52b215af05fdaee66f7f31de24455083c395837565b91f35b50c1
+size 11150678

valid.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:963d2115e4fecddb9572cc18d0a2c82d483e6ab565e4835b5b5ed51eb6cabd0e
-size 1314327
+oid sha256:d14f070e6b11334d054efeb795574833450a41a8d6403c201e1a8cafc2733fd4
+size 1259984