OpenDataBox / OpenDataBox/Qute
Release Qute datasets on Hugging Face
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- Dominant language
- Python
- Stars
- 14
- Forks
- 0
- PR merge metrics
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Description
Hi @weizhoudb 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2602.14699.
The paper page lets people discuss about your paper and lets them find artifacts about it (your datasets 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.
Would you like to host the datasets you've released (the low-selectivity CSV data in your GitHub repository) on https://huggingface.co/datasets?
Hosting on Hugging Face will give you more visibility/enable better discoverability, and will also allow people to do:
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/qute-low-selectivity")
If you're down, leaving a guide here: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows people to quickly explore the data files 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/need any guidance :)
Kind regards,
Niels
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the low-selectivity CSV data in the Qute repository and review Hugging Face's datasets loading guide and dataset viewer documentation. Done means the datasets are uploaded to Hugging Face and linked to the paper page, but the issue first needs confirmation that the project wants to proceed.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- huggingface, python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100