microsoft / microsoft/HealthAgentBench

Release HealthAgentBench on Hugging Face

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Description

Hi @sheng-z 🤗

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 datasets or environments for instance), you can also claim the paper as yours which will show up on your public profile at HF, and add GitHub and project page URLs.

I saw on your GitHub repository that the HealthAgentBench benchmark will be released soon. Would you like to host the datasets you've compiled for this benchmark on https://huggingface.co/datasets?

Hosting on Hugging Face will give you more visibility and enable better discoverability within the community. It will also allow researchers to easily load the datasets:

from datasets import load_dataset

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

If you're down, leaving a guide here: https://huggingface.co/docs/datasets/loading.
We also support Webdataset, which is extremely useful for large-scale multimodal clinical datasets (such as 2D X-rays, 3D CT volumes, and gigapixel pathology slides): https://huggingface.co/docs/datasets/en/loading#webdataset.

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

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

Let me know if you're interested or need any guidance!

Kind regards,

Niels

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First steps

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Research direction

No repository files, tests, or entry points are named. Read the linked Hugging Face datasets loading and dataset-viewer documentation first; done would mean the HealthAgentBench datasets are hosted and linked to the paper page.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
20/100

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