facebookresearch / facebookresearch/PAHF
Release PAHF benchmarks on Hugging Face
- Dominant language
- Python
- Stars
- 58
- Forks
- 3
- PR merge metrics
- No merged PRs in 30d
Description
Hi @Saghar-Hosseini 🤗
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/2602.16173.
The paper page lets people discuss about your paper and lets them find artifacts about it (your benchmarks 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.
Would you like to host the datasets (the Embodied Manipulation and Online Shopping benchmarks) you've released on https://huggingface.co/datasets?
I see you're currently hosting the JSON and Python files on GitHub. Hosting on Hugging Face will give you more visibility/enable better discoverability, and will also allow people to do:
```python
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/PAHF-Embodied")
```
If you're down, leaving a guide here: 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 task scenarios and personas directly in the browser.
After uploaded, we can also link the datasets 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
Contributor guide
Research direction
Start by reviewing the JSON and Python files currently hosted in the repository and the Hugging Face Datasets loading guide. The work is done when the Embodied Manipulation and Online Shopping benchmarks are uploaded to Hugging Face, can be explored in the dataset viewer, and are linked to the paper page.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- huggingface, python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100