metadata
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
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:
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},
}