Release Tora2 Training Video Dataset on Hugging Face

Hi @Leojc 🤗

I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2507.05963.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models 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's fantastic to see that you've already released the Tora model checkpoints (and MuseV) on the Hugging Face Hub! This significantly improves their visibility and discoverability for the community.

I noticed that your paper also introduces a new dataset of "1.1 million video clips featuring diverse entities" which was curated for training Tora2. Would you be interested in hosting this dataset on https://huggingface.co/datasets as well?
This would enable better visibility and discoverability for the dataset, allowing people to easily use it for research and development. It would also allow users to load it with a simple `load_dataset` call:

```python
from datasets import load_dataset

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

If you're interested, here's a guide: https://huggingface.co/docs/datasets/loading.
Besides that, there's the [dataset viewer](https://huggingface.co/docs/hub/en/datasets-viewer) which allows people to quickly explore the first few rows of the data in the browser.

After uploaded, we can also link the dataset to the paper page (read [here](https://huggingface.co/docs/hub/en/model-cards#linking-a-paper)) so people can discover your work.

Let me know if you're interested/need any guidance.

Kind regards,

Niels

Start with the Hugging Face Datasets loading guide and dataset viewer documentation linked in the issue, then identify the Tora2 training dataset artifacts to publish. Done means the dataset is hosted on the Hugging Face Hub, can be loaded with load_dataset, is viewable in the browser, and is linked from the paper page.

Written by the indexing model from the issue text.