Update README.md

README.md

@@ -30,4 +30,96 @@ configs:
   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 1,640 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}
+}
+
+
+```