Hi @shibowing 馃
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/2608.22896
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, and add Github and project page URLs.
I noticed that the code will be released after RSS. That's great to hear! When you do release it, would you consider also hosting any associated artifacts鈥攕uch as pre-trained checkpoints or benchmark datasets鈥攐n the Hugging Face Hub? Hosting on Hugging Face gives you more visibility and better discoverability, and we can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
For models, you can use the PyTorchModelHubMixin to easily upload and load checkpoints. For datasets, we support load_dataset with a simple one-liner, and the dataset viewer lets people explore the first rows in the browser.
After uploading, we can also link the artifacts to the paper page so people can discover your work directly. Let me know if you're interested or need any guidance!
Kind regards,
Niels
ML Engineer @ Hugging Face 馃
Hi @shibowing 馃
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/2608.22896
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, and add Github and project page URLs.
I noticed that the code will be released after RSS. That's great to hear! When you do release it, would you consider also hosting any associated artifacts鈥攕uch as pre-trained checkpoints or benchmark datasets鈥攐n the Hugging Face Hub? Hosting on Hugging Face gives you more visibility and better discoverability, and we can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
For models, you can use the PyTorchModelHubMixin to easily upload and load checkpoints. For datasets, we support
load_datasetwith a simple one-liner, and the dataset viewer lets people explore the first rows in the browser.After uploading, we can also link the artifacts to the paper page so people can discover your work directly. Let me know if you're interested or need any guidance!
Kind regards,
Niels
ML Engineer @ Hugging Face 馃