--- 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](https://github.com/jonathan-roberts1/GRAB) - **Paper:** [GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models]() ### Dataset Summary TODO ### Example usage ```python 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': , '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](https://github.com/jonathan-roberts1/GRAB) 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:}, } ```