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Chemistry/test-00000-of-00001.parquet

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Physics/test-00000-of-00001.parquet

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README.md

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+---
+language:
+- en
+size_categories:
+- 1K<n<10K
+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_bytes: 49337131.36
+    num_examples: 1176
+  download_size: 38090732
+  dataset_size: 49337131.36
+- 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_bytes: 201047028.0
+    num_examples: 564
+  download_size: 156921633
+  dataset_size: 201047028.0
+- 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_bytes: 55727097.0
+    num_examples: 892
+  download_size: 49594723
+  dataset_size: 55727097.0
+- 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_bytes: 20512520.0
+    num_examples: 156
+  download_size: 13597019
+  dataset_size: 20512520.0
+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-*
+tags:
+- chemistry
+- physics
+- math
+- coding
+---
+
+## Dataset Description
+
+We introduce **EMMA (Enhanced MultiModal reAsoning)**, a 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, offering an enhanced test suite for MLLMs' reasoning capabilities.
+
+EMMA is composed of 2,788 problems, of which 1,796 are newly constructed, across four domains. Within each subject, we further provide fine-grained labels for each question based on the specific skills it measures.
+
+<p align="center">
+    <img src="https://huggingface.co/datasets/luckychao/EMMA/resolve/main/emma_composition.jpg" width="30%"> <br>
+</p>
+
+## Paper Information
+
+- Paper: https://www.arxiv.org/abs/2501.05444
+- Code: https://github.com/hychaochao/EMMA
+- Project: https://emma-benchmark.github.io/
+
+## Dataset Usage
+
+### Data Downloading
+
+You can download the dataset by the following command (Taking downloading math data as an example):
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("luckychao/EMMA", "Math", split="test")
+```
+
+
+### 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,
+}
+```
+
+### Automatic Evaluation
+
+ To automatically evaluate a model on the dataset, please refer to our GitHub repository [here](https://github.com/hychaochao/EMMA).
+
+## 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}, 
+}
+```