Dataset Card for HQ-EDIT

HQ-Edit, a high-quality instruction-based image editing dataset with total 197,350 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. HQ-Edit’s high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/HQ-Edit.

svjack/HQ-Edit-Sample-2500 dataset hosted on Hugging Face and contributed by the HF Datasets community

The global proofreading and editing service market is projected to reach a value of USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The growth of this market is primarily driven by the increasing demand for high-quality content across industries such as education, publishing, and business. Furthermore, the growing adoption of digital publishing and the need for accurate and error-free content online has further fueled the demand for proofreading and editing services. Regionally, North America and Europe are expected to remain dominant markets due to the presence of established publishing houses and a high demand for high-quality content. However, emerging markets in Asia-Pacific, particularly China and India, are projected to witness significant growth due to the increasing adoption of English as a global language and the rising number of academic and scientific publications. The market is fragmented with numerous local and international players, including Elsevier, Cambridge Proofreading, Language Services Consultants, Inc., Enago, Scribendi, Wordy, HQ-Translate S.R.O., Ehlion Language Consultancy, Etcetera Language Group, Inc., Science Journal Editors, Latitude Prime, Blend Express, International Science Editing, KERN Group, Gramlee, Baltic Media, Ulatus, Quvae Research and Publications, and others.

Results of measurements on 100 random copies of the Celeba-HQ Dataset.

This is a dataset containing 60,000 Stack Overflow questions from 2016-2020. Questions are classified into three categories:

1. HQ: High-quality posts without a single edit.
2. LQ_EDIT: Low-quality posts with a negative score, and multiple community edits. However, they still remain open after those changes.
3. LQ_CLOSE: Low-quality posts that were closed by the community without a single edit.

How to cite

Annamoradnejad, I., Habibi, J., & Fazli, M. (2022). Multi-view approach to suggest moderation actions in community question answering sites. Information Sciences, 600, 144-154.

@article{annamoradnejad2022multiview,
 title={Multi-View Approach to Suggest Moderation Actions in Community Question Answering Sites},
 author={Annamoradnejad, Issa and Habibi, Jafar and Fazli, Mohammadamin},
 journal = {Information Sciences},
 volume = {600},
 pages = {144-154},
 year = {2022},
 issn = {0020-0255},
 doi = {https://doi.org/10.1016/j.ins.2022.03.085},
 url = {https://www.sciencedirect.com/science/article/pii/S0020025522003127}
}

Notes:

• Questions are sorted according to Question Id.
• Question body is in HTML format.
• All dates are in UTC format.

Source:

https://github.com/Moradnejad/StackOverflow-Questions-Quality-Dataset