Hi @maks-ivanov 🤗
I'm Niels and work as part of 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 dataset for instance), you can also claim the paper as yours which will show up on your public profile at HF, and add GitHub/project page URLs.
Would you like to host the ERP-Bench dataset you've released on https://huggingface.co/datasets? I noticed your GitHub repository already includes Hugging Face metadata tags and Croissant support in the README, which is fantastic!
Hosting directly on Hugging Face will give you more visibility and enable better discoverability. It will also allow people to easily load the benchmark using:
from datasets import load_dataset
dataset = load_dataset("agentic-labs/erp-bench")If you're down, leaving a guide here: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows people to quickly explore the task instructions and metadata in the browser. After uploaded, we can also link the dataset to the paper page (read here).
Let me know if you're interested/need any guidance.
Kind regards,
Niels
Hi @maks-ivanov 🤗
I'm Niels and work as part of 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 dataset for instance), you can also claim the paper as yours which will show up on your public profile at HF, and add GitHub/project page URLs.
Would you like to host the ERP-Bench dataset you've released on https://huggingface.co/datasets? I noticed your GitHub repository already includes Hugging Face metadata tags and Croissant support in the README, which is fantastic!
Hosting directly on Hugging Face will give you more visibility and enable better discoverability. It will also allow people to easily load the benchmark using:
If you're down, leaving a guide here: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows people to quickly explore the task instructions and metadata in the browser. After uploaded, we can also link the dataset to the paper page (read here).
Let me know if you're interested/need any guidance.
Kind regards,
Niels