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import os
import base64
current_dir = os.path.dirname(os.path.realpath(__file__))
with open(os.path.join(current_dir, "bottom_logo.png"), "rb") as image_file:
bottom_logo = base64.b64encode(image_file.read()).decode("utf-8")
benchname = 'KOFFVQA'
Bottom_logo = f'''<img src="data:image/jpeg;base64,{bottom_logo}" style="width:20%;display:block;margin-left:auto;margin-right:auto">'''
intro_md = f'''
# {benchname} Leaderboard
* [Dataset](https://huggingface.co/datasets/maum-ai/KOFFVQA_Data)
* [Evaluation Code](https://github.com/maum-ai/KOFFVQA)
* Report (coming soon)
{benchname}🔍 is a Free-Form VQA benchmark dataset designed to evaluate Vision-Language Models (VLMs) in Korean language environments. Unlike traditional multiple-choice or predefined answer formats, KOFFVQA challenges models to generate open-ended, natural-language answers to visually grounded questions. This allows for a more comprehensive assessment of a model's ability to understand and generate nuanced Korean responses.
The dataset encompasses diverse real-world scenarios, including object attributes, recognition, relationship, etc.
The page will be continuously updated and will accept requests to add models to the leaderboard. For more details, please refer to the "Submit" tab.
'''.strip()
about_md = f'''
# About
The {benchname} benchmark is designed to evaluate and compare the performance of Vision-Language Models (VLMs) in Korean language environments.
This benchmark includes a total of 275 Korean questions across 10 tasks. The questions are open-ended, free-form VQA (Visual Question Answering) with objective answers, allowing responses without strict format constraints.
## News
* **2025-01-21**: [Evaluation code](https://github.com/maum-ai/KOFFVQA) and [dataset](https://huggingface.co/datasets/maum-ai/KOFFVQA_Data) release
* **2024-12-06**: Leaderboard Release!
'''.strip()
submit_md = f'''
# Submit (coming soon)
We are not accepting model addition requests at the moment. Once the request system is established, we will start accepting requests.
'''.strip()
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