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---
dataset_info:
features:
- name: query
dtype: string
- name: image_filename
dtype: string
- name: image
dtype: image
- name: answer
dtype: string
- name: answer_type
dtype: string
- name: page
dtype: string
- name: model
dtype: string
- name: prompt
dtype: string
- name: source
dtype: string
splits:
- name: test
num_bytes: 774039186.125
num_examples: 1663
download_size: 136066416
dataset_size: 774039186.125
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: cc-by-4.0
task_categories:
- visual-question-answering
- question-answering
language:
- en
tags:
- Document Retrieval
- VisualQA
- QA
size_categories:
- 1K<n<10K
---
## Dataset Description
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.
Example of data (see viewer)
### Data Curation
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.
### Load the dataset
```python
from datasets import load_dataset
ds = load_dataset("vidore/tatdqa_test", split="test")
```
### Dataset Structure
Here is an example of a dataset instance structure:
```json
features:
- name: questionId
dtype: string
- name: query
dtype: string
- name: question_types
dtype: 'null'
- name: image
dtype: image
- name: docId
dtype: int64
- name: image_filename
dtype: string
- name: page
dtype: string
- name: answer
dtype: 'null'
- name: data_split
dtype: string
- name: source
dtype: string
```
## Citation Information
If you use this dataset in your research, please cite the original dataset as follows:
```latex
@inproceedings{zhu-etal-2021-tat,
title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance",
author = "Zhu, Fengbin and
Lei, Wenqiang and
Huang, Youcheng and
Wang, Chao and
Zhang, Shuo and
Lv, Jiancheng and
Feng, Fuli and
Chua, Tat-Seng",
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)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.254",
doi = "10.18653/v1/2021.acl-long.254",
pages = "3277--3287"
}
@inproceedings{zhu2022towards,
title={Towards complex document understanding by discrete reasoning},
author={Zhu, Fengbin and Lei, Wenqiang and Feng, Fuli and Wang, Chao and Zhang, Haozhou and Chua, Tat-Seng},
booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
pages={4857--4866},
year={2022}
}
``` |