Update cleaned 3DQA dataset

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README.md

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+---
+license: mit
+language:
+  - en
+pretty_name: Mesh2QA ScanNet 3D Question Answering
+task_categories:
+  - question-answering
+tags:
+  - 3d
+  - visual-question-answering
+  - scannet
+  - mesh2qa
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: trainval.jsonl
+      - split: test
+        path: test.jsonl
+---
+
+# 3D Question Answering ScanNet Export
+
+This dataset card publishes a Hugging Face Dataset Viewer-ready JSONL export for the
+[3D Question Answering](https://shuquanye.com/3DQA_website/) project by Shuquan Ye,
+Dongdong Chen, Songfang Han, and Jing Liao.
+
+Related links:
+
+- [Project page](https://shuquanye.com/3DQA_website/)
+- [arXiv](https://arxiv.org/abs/2112.08359v2)
+- [Code](https://github.com/pleaseconnectwifi/3DQA)
+- [Dataset repository](https://huggingface.co/datasets/pleaseconnectwifi/3DQA)
+
+## Splits
+
+- `train`: 9160 examples from `scene0000_00` through `scene0706_00`; this intentionally combines the original train and validation portion. Because some cloud files were lost, the train and validation split cannot be distinguished.
+- `test`: 902 examples from `scene0707_00` through `scene0806_00`.
+
+The split keeps the current project convention of using the ScanNet test scene block separately while publishing train and validation together.
+
+## Fields
+
+- `id`, `qakey`: example identifiers.
+- `scene_id`: ScanNet scene identifier.
+- `question_id`, `question`, `question_status`: question metadata.
+- `perspective_specific`, `camera`: viewpoint metadata for the question.
+- `answers`, `answer_confidences`, `answer_annotations`, `has_answer`: answer data and annotations.
+- `source`: original source metadata.
+
+## Citation
+
+If you use this dataset, please cite the original 3DQA paper:
+
+```bibtex
+@article{ye20213dqa,
+  title = {3D Question Answering},
+  author = {Ye, Shuquan and Chen, Dongdong and Han, Songfang and Liao, Jing},
+  journal = {IEEE Transactions on Visualization and Computer Graphics},
+  doi = {10.48550/arXiv.2112.08359},
+}
+```

trainval.jsonl

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