Create README.md
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README.md
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---
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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dataset_info:
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features:
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- name: url
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dtype: string
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- name: caption
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dtype: string
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- name: similarity
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dtype: float64
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- name: page_title
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dtype: string
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- name: page_url
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dtype: string
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- name: punsafe
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dtype: float64
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- name: width
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dtype: float64
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- name: height
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dtype: float64
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- name: original_width
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dtype: float64
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- name: original_height
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dtype: float64
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- name: sha256
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dtype: string
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- name: phash
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dtype: string
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splits:
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- name: train
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num_bytes: 72405439283
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num_examples: 153942892
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download_size: 46743814850
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dataset_size: 72405439283
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license: apache-2.0
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language:
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- ja
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size_categories:
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- 100M<n<1B
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---
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<div align="center" style="line-height: 1;">
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<h1>WAON: Large-Scale and High-Quality Japanese Image-Text Pair Dataset for Vision-Language Models </h1>
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<a href="https://huggingface.co/collections/llm-jp/waon" target="_blank">🤗 HuggingFace</a>
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<a href="https://arxiv.org/abs/2510.22276" target="_blank">📄 Paper</a>
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<a href="https://github.com/llm-jp/WAON" target="_blank">🧑💻 Code</a>
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<br/>
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<img src="validation_top1_accuracy.svg" width="50%"/>
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</div>
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## Introduction
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WAON is a Japanese (image, text) pair dataset containing approximately 155M examples, crawled from Common Crawl.
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It is built from snapshots taken in 2025-18, 2025-08, 2024-51, 2024-42, 2024-33, and 2024-26.
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The dataset is high-quality and diverse, constructed through a sophisticated data processing pipeline.
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We apply filtering based on image size and SigLIP scores, and perform deduplication using URLs, captions, and perceptual hashes (pHash).
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## How to Use
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Clone the repository:
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```bash
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git clone https://gitlab.llm-jp.nii.ac.jp/datasets/waon.git
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cd waon
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```
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Load the dataset using the `datasets` library:
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```python
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from datasets import load_dataset
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ds = load_dataset("parquet", data_dir="data")
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```
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### Format
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- `url`: URL of the image
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- `caption`: Caption associated with the image
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- `page_title`: Title of the page containing the image
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- `page_url`: URL of the page
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- `punsafe`: Probability that the image is unsafe
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- `quality`: The quality of the text in the text column
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- `width`: Width (in pixels) of the resized image used for computing pHash
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- `height`: Height (in pixels) of the resized image used for computing pHash
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- `original_width`: Original width of the image
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- `original_height`: Original height of the image
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- `sha256`: SHA-256 hash of the original image file
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- `phash`: Perceptual hash (pHash) computed from the resized image
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## Dataset Construction Pipeline
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We construct WAON dataset through the following steps:
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<div align="center">
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<img src="waon-pipeline.svg" width="50%"/>
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</div>
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## LICENSE
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This dataset is licensed under the Apache License 2.0 and governed by Japanese law. Its use is limited to “information analysis” as defined in Article 30-4 of the Japanese Copyright Act.
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## Citation
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```bibtex
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@misc{sugiura2025waonlargescalehighqualityjapanese,
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title={WAON: Large-Scale and High-Quality Japanese Image-Text Pair Dataset for Vision-Language Models},
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author={Issa Sugiura and Shuhei Kurita and Yusuke Oda and Daisuke Kawahara and Yasuo Okabe and Naoaki Okazaki},
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year={2025},
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eprint={2510.22276},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2510.22276},
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}
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```
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