Hi @ 🤗
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.
I noticed in your abstract that "Our code is open-sourced at this https URL" (referring to https://github.com/ScopeX-ASU/Apollo), and you also mention developing and open-sourcing "large-scale PIC benchmarks". Currently, the GitHub README at the provided link returns a 404 error, indicating the code and benchmarks might not be fully public yet.
It'd be great to make the Apollo framework's code and your new PIC benchmark datasets available on the 🤗 hub, to improve their discoverability/visibility once they are ready for release.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models (code for the Apollo framework)
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
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.
We encourage researchers to push each model/code repository to a separate model repository, so that things like download stats also work. We can then also link the code to the paper page.
Uploading dataset (PIC benchmarks)
Would be awesome to make the datasets 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 once the code and datasets are publicly available!
Cheers,
Niels
ML Engineer @ HF 🤗
Hi @ 🤗
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.
I noticed in your abstract that "Our code is open-sourced at this https URL" (referring to
https://github.com/ScopeX-ASU/Apollo), and you also mention developing and open-sourcing "large-scale PIC benchmarks". Currently, the GitHub README at the provided link returns a 404 error, indicating the code and benchmarks might not be fully public yet.It'd be great to make the Apollo framework's code and your new PIC benchmark datasets available on the 🤗 hub, to improve their discoverability/visibility once they are ready for release.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models (code for the Apollo framework)
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, we could leverage the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto any customnn.Module. Alternatively, one can leverages the hf_hub_download one-liner to download a checkpoint from the hub.We encourage researchers to push each model/code repository to a separate model repository, so that things like download stats also work. We can then also link the code to the paper page.
Uploading dataset (PIC benchmarks)
Would be awesome to make the datasets available on 🤗 , so that people can do:
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 once the code and datasets are publicly available!
Cheers,
Niels
ML Engineer @ HF 🤗