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README.md

@@ -2,7 +2,7 @@
 language:
 - en
 size_categories:
-- 1K<n<10K
+- n<1K
 task_categories:
 - question-answering
 - visual-question-answering
@@ -44,10 +44,8 @@ dataset_info:
     dtype: string
   splits:
   - name: test
-    num_bytes: 49337131.36
-    num_examples: 1176
-  download_size: 38090732
-  dataset_size: 49337131.36
+    num_examples: 8
+  download_size: 415466
 - config_name: Coding
   features:
   - name: pid
@@ -84,10 +82,8 @@ dataset_info:
     dtype: string
   splits:
   - name: test
-    num_bytes: 201047028.0
-    num_examples: 564
-  download_size: 156921633
-  dataset_size: 201047028.0
+    num_examples: 8
+  download_size: 1693180
 - config_name: Math
   features:
   - name: pid
@@ -124,10 +120,8 @@ dataset_info:
     dtype: string
   splits:
   - name: test
-    num_bytes: 55727097.0
-    num_examples: 892
-  download_size: 49594723
-  dataset_size: 55727097.0
+    num_examples: 8
+  download_size: 857062
 - config_name: Physics
   features:
   - name: pid
@@ -164,10 +158,8 @@ dataset_info:
     dtype: string
   splits:
   - name: test
-    num_bytes: 20512520.0
-    num_examples: 156
-  download_size: 13597019
-  dataset_size: 20512520.0
+    num_examples: 8
+  download_size: 566203
 - config_name: All
   features:
   - name: pid
@@ -204,10 +196,8 @@ dataset_info:
     dtype: string
   splits:
   - name: test
-    num_bytes: 326623776.36
-    num_examples: 2788
-  download_size: 258203107
-  dataset_size: 326623776.36
+    num_examples: 32
+  download_size: 3534939
 configs:
 - config_name: Chemistry
   data_files:
@@ -236,22 +226,62 @@ tags:
 - coding
 ---
 
-## Dataset Description
+# EMMA Clone Dataset (Small Version)
 
-**EMMA (Enhanced MultiModal reAsoning)** is 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 Stone** is a reduced version of the EMMA (Enhanced MultiModal reAsoning) benchmark with 8 samples per subject category, designed for quick testing and development.
 
-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.
+This dataset contains:
+- **Chemistry**: 8 samples
+- **Coding**: 8 samples  
+- **Math**: 8 samples
+- **Physics**: 8 samples
+- **All**: 32 samples (8 from each category)
 
-<p align="center">
-    <img src="https://huggingface.co/datasets/luckychao/EMMA/resolve/main/emma_composition.jpg" width="30%"> <br>
-</p>
+## Usage
 
-## Paper Information
+### Loading with datasets library
 
-- Paper: https://www.arxiv.org/abs/2501.05444
-- Code: https://github.com/hychaochao/EMMA
-- Project: https://emma-benchmark.github.io/
+```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