README.md

metadata

dataset_info:
  features:
    - name: pid
      dtype: int64
    - name: question
      dtype: string
    - name: decoded_image
      dtype: image
    - name: image
      dtype: string
    - name: answer
      dtype: string
    - name: task
      dtype: string
    - name: category
      dtype: string
    - name: complexity
      dtype: int64
  splits:
    - name: GRAB
      num_bytes: 466596459.9
      num_examples: 2170
  download_size: 406793109
  dataset_size: 466596459.9
configs:
  - config_name: default
    data_files:
      - split: GRAB
        path: data/GRAB-*

Dataset Card for GRAB

Dataset Description

• Homepage: https://github.com/jonathan-roberts1/GRAB
• Paper: GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models

Dataset Summary

TODO

Example usage

from datasets import load_dataset

# load dataset
grab_dataset = load_dataset("jonathan-roberts1/GRAB", split='GRAB')
"""
Dataset({
    features: ['pid', 'question', 'decoded_image', 'image', 'answer', 'task', 'category', 'complexity'],
    num_rows: 2170
})
"""
# query individual questions
grab_dataset[40] # e.g., the 41st element
"""
{'pid': 40, 'question': 'What is the value of the y-intercept of the function? Give your answer as an integer.',
'decoded_image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=5836x4842 at 0x12288EA60>,
'image': 'images/40.png', 'answer': '1', 'task': 'properties', 'category': 'Intercepts and Gradients',
'complexity': 0}
"""
question_40 = grab_dataset[40]['question'] # question
answer_40 = grab_dataset[40]['answer'] # ground truth answer
pil_image_40 = grab_dataset[0]['decoded_image']

Note -- the 'image' feature corresponds to filepaths in the images dir in this repository: (https://huggingface.co/datasets/jonathan-roberts1/GRAB/resolve/main/images.zip)

Please visit our GitHub repository for example inference code.

Dataset Curators

This dataset was curated by Jonathan Roberts, Kai Han, and Samuel Albanie

Citation Information

@article{roberts2024grab,
      title={GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models}, 
      author={Jonathan Roberts and Kai Han and Samuel Albanie},
      year={2024},
      journal={arXiv preprint arXiv:},
}