--- 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 following examples illustrate two different formats of the training dataset. The first includes a ‘title‘ field, while the second does not. ## Dataset Example (with Title) ```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...." } ] } ``` ## Dataset Example (without Title) ```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 ### English Example ```Python from datasets import load_dataset dataset = load_dataset("naist-nlp/ExpArt") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['id', 'title', 'conversations'], # num_rows: X # Replace X with the actual number of rows in your dataset # }) # }) # Example of accessing a single data instance example = dataset['train'][0] print(example) # { # "id": "0001_T", # "title": "Mona Lisa", # "conversations": [ # { # "from": "user", # "value": "\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...." # } # ] # } ```