|
--- |
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dataset_info: |
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features: |
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- name: query |
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dtype: string |
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- name: image_filename |
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dtype: string |
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- name: image |
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dtype: image |
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- name: answer |
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dtype: string |
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- name: answer_type |
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dtype: string |
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- name: page |
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dtype: string |
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- name: model |
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dtype: string |
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- name: prompt |
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dtype: string |
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- name: source |
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dtype: string |
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splits: |
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- name: test |
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num_bytes: 774039186.125 |
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num_examples: 1663 |
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download_size: 136066416 |
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dataset_size: 774039186.125 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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license: cc-by-4.0 |
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task_categories: |
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- visual-question-answering |
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- question-answering |
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language: |
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- en |
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tags: |
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- Document Retrieval |
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- VisualQA |
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- QA |
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size_categories: |
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- 1K<n<10K |
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--- |
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|
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## Dataset Description |
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This is the test set taken from the [TAT-DQA dataset](https://nextplusplus.github.io/TAT-DQA/)is a large-scale Document VQA dataset that was constructed from publicly available real-world financial reports. It focuses on rich tabular and textual content requiring numerical reasoning. Questions and answers were manually annotated by human experts in finance. |
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Example of data (see viewer) |
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### Data Curation |
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Unlike other 'academic' datasets, we kept the full test set as this dataset closely represents our use case of document retrieval. There are 1,663 image-query pairs. |
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### Load the dataset |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("vidore/tatdqa_test", split="test") |
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``` |
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### Dataset Structure |
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Here is an example of a dataset instance structure: |
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```json |
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features: |
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- name: questionId |
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dtype: string |
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- name: query |
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dtype: string |
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- name: question_types |
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dtype: 'null' |
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- name: image |
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dtype: image |
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- name: docId |
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dtype: int64 |
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- name: image_filename |
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dtype: string |
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- name: page |
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dtype: string |
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- name: answer |
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dtype: 'null' |
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- name: data_split |
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dtype: string |
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- name: source |
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dtype: string |
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``` |
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## Citation Information |
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If you use this dataset in your research, please cite the original dataset as follows: |
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```latex |
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@inproceedings{zhu-etal-2021-tat, |
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title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance", |
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author = "Zhu, Fengbin and |
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Lei, Wenqiang and |
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Huang, Youcheng and |
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Wang, Chao and |
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Zhang, Shuo and |
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Lv, Jiancheng and |
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Feng, Fuli and |
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Chua, Tat-Seng", |
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booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", |
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month = aug, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.acl-long.254", |
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doi = "10.18653/v1/2021.acl-long.254", |
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pages = "3277--3287" |
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} |
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|
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@inproceedings{zhu2022towards, |
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title={Towards complex document understanding by discrete reasoning}, |
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author={Zhu, Fengbin and Lei, Wenqiang and Feng, Fuli and Wang, Chao and Zhang, Haozhou and Chua, Tat-Seng}, |
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booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, |
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pages={4857--4866}, |
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year={2022} |
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} |
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|
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|
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``` |