Release UniCrop artifacts (models, dataset) on Hugging Face

[NielsRogge]

Hi @CoDIS-Lab 🤗

Niels here from 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.

It'd be great to make the checkpoints and dataset available on the 🤗 hub, to improve their discoverability/visibility. We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.

Uploading models

I noticed you've released trained model artifacts (LightGBM, Random Forest, etc.) in a pickle file in your repository. You can host these on the Hub easily! See here for a guide: https://huggingface.co/docs/hub/models-uploading.

For Scikit-Learn or boosted tree models like LightGBM, you might also find the skops library useful. We encourage researchers to push each model checkpoint to a separate model repository so that things like download stats also work. We can then also link the checkpoints to the paper page.

Uploading dataset

Would be awesome to make the processed "analysis-ready" dataset 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.

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

Cheers,

Niels
ML Engineer @ HF 🤗