Verify evals on Papers with Code
- Dominant language
- Python
- Stars
- 23
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
Niels here from the open-source team at Hugging Face. Congratulations on your work!
I've made the [paper](https://paperswithcode.co/paper/2605.23897) and [2 paper-native evaluations](https://paperswithcode.co/paper/2605.23897#results) available on Papers with Code.
The paper is part of the [Image Understanding](https://paperswithcode.co/tasks/image-understanding) task page.
The ETCHR + Kimi K2.5 (1T) result currently ranks first on [ChartQA](https://paperswithcode.co/benchmark/chartqa?task=image-understanding&eval=23336).
Would it be possible to verify these results and let me know if any score, model name, benchmark protocol, or openness metadata should be corrected? The imported rows are tied to the paper or its official release artifacts; comparison-table baselines were not added.
You can also edit the task, methods, project page, and GitHub URL directly from the paper page using your Hugging Face account.
If you'd like to showcase the results in your repository README, you can copy these live leaderboard badges (or use the “Copy PwC badge” button in the Results section):
[](https://paperswithcode.co/api/v1/papers/2605.23897/leaderboard-badge-link?eval=23336)
Kind regards,
Niels
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by checking the linked Papers with Code paper, Image Understanding task page, and ChartQA leaderboard entry against the repository's published paper and release artifacts. Confirm the score, model name, benchmark protocol, and openness metadata, then report any corrections; no repository file or test is named.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- huggingface
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100