OpenDocVQA/README.md

---
dataset_info:
- config_name: chartqa
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: relevant_doc_ids
    sequence: string
  - name: dataset_names
    sequence: string
  splits:
  - name: test
    num_bytes: 36856
    num_examples: 150
  download_size: 16709
  dataset_size: 36856
- config_name: default
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: relevant_doc_ids
    sequence: string
  - name: dataset_names
    sequence: string
  splits:
  - name: train
    num_bytes: 12038383
    num_examples: 41020
  download_size: 4605381
  dataset_size: 12038383
- config_name: dude
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: relevant_doc_ids
    sequence: string
  - name: dataset_names
    sequence: string
  splits:
  - name: test
    num_bytes: 136528
    num_examples: 496
  download_size: 60971
  dataset_size: 136528
- config_name: infovqa
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: relevant_doc_ids
    sequence: string
  - name: dataset_names
    sequence: string
  splits:
  - name: test
    num_bytes: 246601
    num_examples: 1048
  download_size: 70905
  dataset_size: 246601
- config_name: slidevqa
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: relevant_doc_ids
    sequence: string
  - name: dataset_names
    sequence: string
  splits:
  - name: test
    num_bytes: 271232
    num_examples: 760
  download_size: 78642
  dataset_size: 271232
- config_name: arxivqa
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: relevant_doc_ids
    sequence: string
  - name: dataset_names
    sequence: string
  splits:
  - name: test
    num_bytes: 501365
    num_examples: 816
  download_size: 501365
  dataset_size: 501365
- config_name: plotqa
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: relevant_doc_ids
    sequence: string
  - name: dataset_names
    sequence: string
  splits:
  - name: test
    num_bytes: 508031
    num_examples: 863
  download_size: 508031
  dataset_size: 508031
- config_name: docvqa
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: relevant_doc_ids
    sequence: string
  - name: dataset_names
    sequence: string
  splits:
  - name: test
    num_bytes: 323886
    num_examples: 585
  download_size: 323886
  dataset_size: 323886
configs:
- config_name: chartqa
  data_files:
  - split: test
    path: chartqa/test-*
- config_name: default
  data_files:
  - split: train
    path: data/train-*
- config_name: dude
  data_files:
  - split: test
    path: dude/test-*
- config_name: infovqa
  data_files:
  - split: test
    path: infovqa/test-*
- config_name: slidevqa
  data_files:
  - split: test
    path: slidevqa/test-*
- config_name: arxivqa
  data_files:
  - split: test
    path: arxivqa/test-*
- config_name: plotqa
  data_files:
  - split: test
    path: plotqa/test-*
- config_name: docvqa
  data_files:
  - split: test
    path: docvqa/test-*
task_categories:
- visual-question-answering
- question-answering
language:
- en
size_categories:
- 100K<n<1M
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  \ https://rrc.cvc.uab.es/?ch=17&com=downloads\n\n2.VisualMRC and SlideVQA datasets\
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---
# Dataset Card for OpenDocVQA
This is a training and evaluation QA data file for [VDocRAG](https://vdocrag.github.io/), a new RAG framework that can directly understand diverse real-world documents purely from visual features.

## Dataset Description

OpenDocVQA is the first unified collection of open-domain document visual question answering datasets, encompassing diverse document types and formats.

#### Supported Tasks and Leaderboards
Given a large collection of document images and a question, the goal of the OpenDocVQA task is to output an answer by finding the relevant images. We decompose the task into two stages. 

- **Visual document retrieval**: Given a question and a corpus of document images, the model retrieves the relevant images from which to derive the answer.
- **DocumentVQA**: The model takes a question and the retrieved images as input to generate an answer. 

OpenDocVQA covers multiple open-domain DocumentVQA datasets with diverse document types. To reflect real-world scenarios, we evaluate models with both single-pool and all-pool settings. 
In the single-pool setting, retrieval is performed from a specific pool of documents provided by each original dataset. The all-pool setting requires retrieving from the entire candidate pool, which includes documents from a wide range of domains.

#### Languages
English

## Dataset Structure

### Data Instances
```json
{
  'query_id': "chartqa-test_6",
  'query': 'Instruct: Given a user query, retrieve a chart image that answers the query.\nQuery: How many more Hispanics younger than 18 tend to be Mexican than Spanish?',
  'answers': ['65'],
  'relevant_doc_ids': ['chartqa/9001.png'],
  'dataset_names': ['chartqa']
}
```

### Data Fields
An example of a sample looks as follows:
```json
{
  'query_id': Query ID,
  'query': Query,
  'answers': List of answers,
  'relevant_doc_ids': List of relevant document IDs,
  'dataset_names': List of source dataset names
}
```

### Stats about the datasets in The OpenDocVQA

| Dataset              | Documents | # images | # Train&Dev | # Test  |
|----------------------|-----------|----------|-------------|---------|
| DocVQA               | Industry   | 12,767    | 6,382        | -        |
| InfoVQA               | Infographic   | 5,485    | 9,592        | 1,048        |
| VisualMRC               | Webpage   | 10,229    | 6,126        | -        |
| ChartQA               | Chart   | 20,882    | -        | 150       |
| OpenWikitable               | Table   | 1,257    | 4,261        | -        |
| DUDE               | Open   | 27,955    | 2,135        | 496        |
| MPMQA               | Manual   | 10,018    | 3,054        | -        |
| SlideVQA               | Slide   | 52,380    | -        | 760        |
| MHDocVQA  (newly created)            | Open   | 28,550    | 9,470        | -        |



## Additional Information

### License
Each of the publicly available sub-datasets present in the OpenDocVQA is governed by specific licensing conditions. 
Therefore, when making use of them, you must take into consideration each of the licenses governing each dataset.

The QA pairs of MHDocVQA, VisualMRC, and SlideVQA datasets in this repo are released under the [NTT License](https://huggingface.co/NTT-hil-insight/OpenDocVQA/edit/main/LICENSE).

### Citation
```bibtex
@inproceedings{tanaka2025vdocrag,
  author    = {Ryota Tanaka and
               Taichi Iki and
               Taku Hasegawa and
               Kyosuke Nishida and
               Kuniko Saito and
               Jun Suzuki},
  title     = {VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents},
  booktitle = {CVPR},
  year      = {2025}
}
```