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Release artifacts (models, datasets) on Hugging Face #1

@NielsRogge

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@NielsRogge

Hi @AIDAS-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

See here for a guide: https://huggingface.co/docs/hub/models-uploading.

I noticed the MATA scheduler is currently on Google Drive. Hosting it on Hugging Face would make it much easier for the community to find and use. I also see the confidence checker is already on the hub, but under an account that looks anonymous (7anonymous7). It would be great to host both under an official organization profile!

In this case, we could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverages the hf_hub_download one-liner to download a checkpoint from the hub.

Uploading dataset

Would also be awesome to make the MATA training datasets (currently on GitHub) available on 🤗 , so that people can do:

from datasets import load_dataset

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

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 🤗

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