--- language: - en size_categories: - n<1K task_categories: - question-answering - visual-question-answering - multiple-choice dataset_info: - config_name: Chemistry features: - name: pid dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: solution dtype: string - name: subject dtype: string - name: task dtype: string - name: category dtype: string - name: source dtype: string - name: type dtype: string - name: context dtype: string splits: - name: test num_examples: 8 download_size: 415466 - config_name: Coding features: - name: pid dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: solution dtype: string - name: subject dtype: string - name: task dtype: string - name: category dtype: string - name: source dtype: string - name: type dtype: string - name: context dtype: string splits: - name: test num_examples: 8 download_size: 1693180 - config_name: Math features: - name: pid dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: solution dtype: string - name: subject dtype: string - name: task dtype: string - name: category dtype: string - name: source dtype: string - name: type dtype: string - name: context dtype: string splits: - name: test num_examples: 8 download_size: 857062 - config_name: Physics features: - name: pid dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: solution dtype: string - name: subject dtype: string - name: task dtype: string - name: category dtype: string - name: source dtype: string - name: type dtype: string - name: context dtype: string splits: - name: test num_examples: 8 download_size: 566203 - config_name: All features: - name: pid dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: solution dtype: string - name: subject dtype: string - name: task dtype: string - name: category dtype: string - name: source dtype: string - name: type dtype: string - name: context dtype: string splits: - name: test num_examples: 32 download_size: 3534939 configs: - config_name: Chemistry data_files: - split: test path: Chemistry/test-* - config_name: Coding data_files: - split: test path: Coding/test-* - config_name: Math data_files: - split: test path: Math/test-* - config_name: Physics data_files: - split: test path: Physics/test-* - config_name: All data_files: - split: test path: All/test-* tags: - chemistry - physics - math - coding --- # EMMA Clone Dataset (Small Version) **EMMA Stone** is a reduced version of the EMMA (Enhanced MultiModal reAsoning) benchmark with 8 samples per subject category, designed for quick testing and development. This dataset contains: - **Chemistry**: 8 samples - **Coding**: 8 samples - **Math**: 8 samples - **Physics**: 8 samples - **All**: 32 samples (8 from each category) ## Usage ### Loading with datasets library ```python from datasets import load_dataset # Load specific subject chemistry_data = load_dataset("winvswon78/emma_stone", "Chemistry", split="test") math_data = load_dataset("winvswon78/emma_stone", "Math", split="test") coding_data = load_dataset("winvswon78/emma_stone", "Coding", split="test") physics_data = load_dataset("winvswon78/emma_stone", "Physics", split="test") # Load all subjects combined all_data = load_dataset("winvswon78/emma_stone", "All", split="test") # Verify the dataset print(f"Chemistry samples: {len(chemistry_data)}") print(f"Math samples: {len(math_data)}") print(f"Coding samples: {len(coding_data)}") print(f"Physics samples: {len(physics_data)}") print(f"All samples: {len(all_data)}") print(f"Subject distribution in All: {all_data['subject']}") ``` ### Alternative loading method If you encounter issues with the config names, you can also load the data directly: ```python from datasets import Dataset import pandas as pd # Load specific subject directly chemistry_df = pd.read_parquet("hf://datasets/winvswon78/emma_stone/Chemistry/test-00000-of-00001.parquet") chemistry_dataset = Dataset.from_pandas(chemistry_df) # Load all subjects all_df = pd.read_parquet("hf://datasets/winvswon78/emma_stone/All/test-00000-of-00001.parquet") all_dataset = Dataset.from_pandas(all_df) ``` ## Original EMMA Information This is a sampled version of the original EMMA benchmark targeting organic multimodal reasoning across mathematics, physics, chemistry, and coding. EMMA tasks demand advanced cross-modal reasoning that cannot be solved by thinking separately in each modality. ### Data Format The dataset is provided in jsonl format and contains the following attributes: ``` { "pid": [string] Problem ID, e.g., “math_1”, "question": [string] The question text, "options": [list] Choice options for multiple-choice problems. For free-form problems, this could be a 'none' value, "answer": [string] The correct answer for the problem, "image_1": [image] , "image_2": [image] , "image_3": [image] , "image_4": [image] , "image_5": [image] , "solution": [string] The detailed thinking steps required to solve the problem, "subject": [string] The subject of data, e.g., “Math”, “Physics”..., "task": [string] The task of the problem, e.g., “Code Choose Vis”, "category": [string] The category of the problem, e.g., “2D Transformation”, "source": [string] The original source dataset of the data, e.g., “math-vista”. For handmade data, this could be “Newly annotated” , "type": [string] Types of questions, e.g., “Multiple Choice”, “Open-ended”, "context": [string] Background knowledge required for the question. For problems without context, this could be a 'none' value, } ``` ## Citation ``` @misc{hao2025mllmsreasonmultimodalityemma, title={Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark}, author={Yunzhuo Hao and Jiawei Gu and Huichen Will Wang and Linjie Li and Zhengyuan Yang and Lijuan Wang and Yu Cheng}, year={2025}, eprint={2501.05444}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2501.05444}, } ```