Release EAPrivacy benchmark (code and dataset) on Hugging Face

[NielsRogge]

Hi @Graph-COM 🤗

I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.

The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim
the paper as yours which will show up on your public profile at HF, add Github and project page URLs.

I saw in your GitHub repository (https://github.com/Graph-COM/EAPrivacy) that the code for scenario generation and LLM evaluation for the EAPrivacy benchmark is being refactored and will be released after paper acceptance. That's great news! It would be fantastic to make the EAPrivacy dataset and evaluation code available on the 🤗 Hub once they are fully released, to improve their discoverability and visibility.

Uploading dataset

Would be awesome to make the dataset (the scenario seeds) available on 🤗 , so that people can do:

from datasets import load_dataset

dataset = load_dataset("your-hf-org-or-username/your-dataset")

See here for a guide: https://huggingface.co/docs/datasets/loading.

Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data in the browser.

Uploading code / Spaces

Once the code for scenario generation and LLM evaluation is released, you could also consider packaging it as a Hugging Face Space. This would provide an interactive demo for your benchmark, allowing researchers to easily experiment with EAPrivacy. We can even provide you a ZeroGPU grant, which gives you A100 GPUs for free for more demanding tasks.

After uploaded, we can also link the datasets and any related Spaces to the paper page (read here) so people can discover your work.

Let me know if you're interested/need any help regarding this!

Cheers,

Niels
ML Engineer @ HF 🤗