Hello,
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 Video-PLR and FAE checkpoints, and the VideoPLR-14K and AnetHallu-117K datasets 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. I saw in your README that you've released the trained models and are planning to release the SFT data to Hugging Face!
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
For your Video-PLR and FAE models, which handle video input and produce text (reasoning, evaluations), the video-text-to-text pipeline tag would be appropriate. 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 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 VideoPLR-14K and AnetHallu-117K 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. For these datasets, which involve video content and textual reasoning/judgment, the video-text-to-text task category would be suitable.
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 🤗
Hello,
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 Video-PLR and FAE checkpoints, and the VideoPLR-14K and AnetHallu-117K datasets 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. I saw in your README that you've released the trained models and are planning to release the SFT data to Hugging Face!
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
For your Video-PLR and FAE models, which handle video input and produce text (reasoning, evaluations), the
video-text-to-textpipeline tag would be appropriate. In this case, we could leverage the PyTorchModelHubMixin class which addsfrom_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 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 VideoPLR-14K and AnetHallu-117K datasets available on 🤗 , so that people can do:
See here for a guide: https://huggingface.co/docs/datasets/loading. For these datasets, which involve video content and textual reasoning/judgment, the
video-text-to-texttask category would be suitable.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 🤗