Release LB feature embeddings and per-task VMF KDE data on Hugging Face

[NielsRogge]

Hi @cdjkim 🤗

Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv on ReSpec, a Relevance and Specificity Grounded Online Filtering framework for Learning on Video-Text Data Streams 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, 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 see that you have already included the source code on Github. Would you also be interested in making the LB feature embeddings and per-task VMF KDE data available on the 🤗 hub, to improve their discoverability/visibility and enable full reproducibility of your work? We can add appropriate tags so that people find them easily when filtering https://huggingface.co/datasets.

Would be awesome to make these datasets available on 🤗 Datasets, so that people can do:

from datasets import load_dataset

dataset = load_dataset("your-hf-org-or-username/your-dataset-name")

See here for a guide: https://huggingface.co/docs/datasets/loading.
We also support Webdataset, useful for image/video datasets: https://huggingface.co/docs/datasets/en/loading#webdataset.

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 🤗