Release StaminaBench dataset on Hugging Face
Nessuno ha ancora preso questa issue.
Valutazione
- Difficoltà
- 4/5
- Tempo stimato
- 3-5 giorni
- Idoneità per principianti
- 35/100
Direzione di ricerca
Start by reviewing the ZIP-hosted datasets in the StaminaBench GitHub repository and the Hugging Face dataset-loading and dataset-viewer guides linked in the issue. Determine which pre-generated and LLM-sampled scenarios should be uploaded, then verify they load through the documented Python API and can be linked to the paper page.
Scritto dal modello di indicizzazione a partire dal testo della issue.
Descrizione
Hi @vladisai 🤗
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 and project page URLs.
Would you like to host the datasets you've released (like the pre-generated programmatic and LLM-sampled scenarios) on https://huggingface.co/datasets?
I see they are currently hosted as ZIP files in your GitHub repository. Hosting on Hugging Face will give you more visibility/enable better discoverability, and will also allow people to do:
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/StaminaBench")
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 first few rows of the data in the browser.
After uploaded, we can also link the datasets to the paper page (read here) so people can discover your work.
Let me know if you're interested/need any guidance.
Kind regards,
Niels
- Lingua principale
- Python
- Stelle
- 1
- Fork
- 1
- Metriche di merge delle PR
- Nessuna PR unita negli ultimi 30g
Preparare l'ambiente
- Nessun Dockerfile né file Docker Compose
- Nessun modello di pull request
- Leggi la guida per i contributori
Come iniziare
- Leggi tutta la issue e poi la guida ai contributi del progetto.
- Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
- Fai un fork del repository e lavora su un branch.
- Apri una pull request che faccia riferimento al numero della issue.
Issue simili
-
Project submission:Aperta
Difficoltà 1/5 Meno di un'ora Idoneità per principianti 72/100
slavakurilyak/awesome-ai-agents#845 ·
I maintainer di solito rispondono entro 1 giorno
-
tests: pin that the atomic writers in utils/paths.py stage the temporary file in the target's directoryForse già presa Una pull request collegata a questa issue è aperta o già unita. Apertadifficulty:2 enhancement good first issue
Difficoltà 1/5 1-3 ore Idoneità per principianti 92/100
MakazhanAlpamys/Soup#2178 ·
I maintainer di solito rispondono entro 1 giorno
-
[BUG] dcg.waggr permutation p-values are folded twice, so near-zero scores get the smallest p-valuesAperta
Difficoltà 2/5 1-3 ore Idoneità per principianti 75/100
scverse/rapids-singlecell#854 ·
I maintainer di solito rispondono entro 1 giorno
-
Slang input: int8_t4_packed and uint8_t4_packed are copied into CrossGLForse già presa @ninglinLiu l’ha presa oggi. Apertaarea:frontend backend:slang bug direction:input
Difficoltà 2/5 1-3 ore Idoneità per principianti 70/100
I maintainer di solito rispondono entro 1 giorno
-
random_elements(unique=True) on an OrderedDict: ZeroDivisionError once only zero weights remain, AssertionError with use_weighting=FalseForse già presa Una pull request collegata a questa issue è aperta o già unita. Aperta
Difficoltà 2/5 1-3 ore Idoneità per principianti 85/100
I maintainer di solito rispondono entro 13 giorni