---
license: cc-by-sa-4.0
language:
- en
---
# Dataset Card for Explain Artworks: ExpArt
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
# Dataset Card for "Wiki-ImageReview1.0"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:https://github.com/naist-nlp/Hackathon-2023-Summer**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
## Dataset Summary
>Explain Artworks: ExpArt is designed to enhance the capabilities of large-scale vision-language models (LVLMs) in analyzing and describing artworks.
>Drawing from a comprehensive array of English Wikipedia art articles, the dataset encourages LVLMs to create in-depth descriptions based on images with or without accompanying titles.
>This endeavor aims to improve LVLMs' proficiency in discerning and articulating the historical and thematic nuances of art. Explain Artworks: ExpArt not only aims to elevate AI's understanding and critique of art but also seeks to forge a stronger connection between artificial intelligence and art history.
>With approximately 10,000 articles, the dataset introduces specialized metrics for assessing the effectiveness of LVLMs in art explanation, focusing on their interpretation of visual and textual cues.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset is available in English.
## Dataset Structure
The structure of the raw dataset is as follows:
```JSON
{
"id": "0001_T",
"title": "Mona Lisa",
"conversations": [
{
"from": "user",
"value": "![]()
/images/Mona Lisa.jpg\nFocus on Mona Lisa and explore the history."
},
{
"from": "assistant",
"value": "Of Leonardo da Vinci’s works, the Mona Lisa is the only portrait whose authenticity...."
}
]
}
```
```JSON
{
"id": "0001_NT",
"conversations": [
{
"from": "user",
"value": "![]()
/images/Mona Lisa.jpg\nFocus on this artwork and explore the history."
},
{
"from": "assistant",
"value": "Of Leonardo da Vinci’s works, the Mona Lisa is the only portrait whose authenticity...."
}
]
}
```
### Data Instances
To load datasets, you must specify a language.
### English Example
```Python
from datasets import load_dataset
dataset = load_dataset("naist-nlp/Wiki-ImageReview1.0", 'en')
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['id', 'image', 'image_url', 'genre', 'sentence_1', 'sentence_2', 'sentence_3', 'sentence_4', 'sentence_5', 'annotator_1', 'annotator_2', 'annotator_3', 'best_pair', 'best_pair_rho'],
# num_rows: 207
# })
# })
```
### Japanese Example
```Python
from datasets import load_dataset
dataset = load_dataset("naist-nlp/Wiki-ImageReview1.0", 'ja')
```
An example of the English dataset is as follows:
```JSON
{
"id": "001",
"image": ,
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Ardea_picata.jpg/242px-Ardea_picata.jpg",
"genre": "Animals",
"sentence_1": "This photograph captures the...",
"sentence_2": "The photographer has done...",
"sentence_3": "While the clarity of the image is...",
"sentence_4": "I believe the image fails to...",
"sentence_5": "The photograph stunningly showcases...",
"annotator_1": [1, 3, 4, 5, 2],
"annotator_2": [3, 1, 4, 5, 2],
"annotator_3": [1, 2, 3, 4, 5],
"best_pair": ["annotator_1", "annotator_3"],
"best_pair_rho": 0.4000000059604645
}
```
### Data Fields
- id: Unique ID for each pair of an image and its review.
- image: The image itself.
- image_url: URL from which the image was retrieved.
- genre: The genre to which the image belongs.
- sentence_[1-5]: Review sentences generated by GPT-4V, rated from 1 (best) to 5 (worst) as a review.
- annotator_[1-3]: Rankings of the review sentences in Good Order by annotators 1 to 3.
- "best_pair": [Information Needed]
- "best_pair_rho": [Information Needed]
### Data Splits
| Language | Language code | Size |
| --: | :---------- | :---------- |
| English | en | 207 |
| Japanese | ja | 207 |
## Dataset Creation
> Our dataset construction process consists of the following four steps;
> (1)Collecting images, (2)Generating five review texts, (3)Ranking review texts manually and (4)Filtering low-quality data.
### Curation Rationale
### Source Data
- #### Source of Image
>The images are collected from the "Featured pictures" section of English Wikipedia.
>This section is composed of images, such as photographs, illustrations, and diagrams selected by user votes.
>The image data contained in this section is of very high quality and covers a diverse range of genres including artwork, natural landscapes, historical events, and science.
>We therefore select it as the image source.
>Genre(number of images)
>
>```
>Animals (15) / Artwork (15) / Culture, entertainment, and lifestyle (15) /
>Currency (15) / Diagrams, drawings, and maps (15) /
>Engineering and technology (15) / History (15) / Natural phenomena (15) /
>People (15) / Places (15) / Plants (15) / Sciences (15) / Space (15) /
>Vehicles (15) / Other lifeforms (15) / Other (15)
>```
>