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README.md

@@ -23,17 +23,7 @@ configs:
 
 ## 📊 Dataset Overview
 
-AInsteinBench contains 1,329 scienfitic computing tasks, 244 of them reviewed by multiple domain experts, from two sources:
-
-| Subset | Samples | Description |
-|--------|---------|-------------|
-| **msb_type** | 244 | Multi-SWE-bench scientific computing tasks, verified on both execution and scientific content, reviewed by domain experts (default) |
-| **et_type** | 1,085 | Einstein Toolkit code completion tasks, verified on execution |
-
-### Data Sources
-
-1. **Einstein Toolkit (et_type)**: 1,085 code completion tasks from the Einstein Toolkit codebase
-2. **Multi-SWE-Bench (msb_type)**: 244 tasks from multi-swe-bench processing of multiple scientific computing repositories, with verification and review from domain experts
+AInsteinBench provides 244 scientific computing tasks derived from multiple scientific repositories. These tasks have been verified on execution and also reviewed by corresponding domain experts to verify both software engineering and scientific content accuracy. The tasks cover numerical relativity, quantum information, molecular dynamics, cheminformatics and quantum chemistry. 
 
 ## 📋 Data Fields
 
@@ -55,7 +45,17 @@ The questions are formatted following the AInsteinBench format:
 
 ## 🔬 Data Curation Process
 
-### Einstein Toolkit Processing (et_type)
+### Multi-SWE-Bench Processing (`msb_type`)
+
+244 tasks from real-world development process of scientific computing repositories:
+- **Sources**: OpenMM, PySCF, RDkit, Qiskit, AMReX, EinsteinToolkit (EisnteinToolkit problems are synthesized. Others are from real issues and pull requests)
+- **Languages**: C++ (\~65%), Python (\~35%)
+- **Tasks**: Bug fixes, feature implementation, code completion
+
+Data is formatted following the Multi-SWE-Bench structure with issue descriptions, patches, and test cases.
+
+
+### Einstein Toolkit Processing (`et_type`)
 
 The Einstein Toolkit is a collection of C/C++/Fortran codes for general relativistic simulations, organized into packages called "Thorns" managed by the Cactus Computation Language (CCL).
 
@@ -79,19 +79,10 @@ The Einstein Toolkit is a collection of C/C++/Fortran codes for general relativi
 **Problem Definition**: Given an incomplete Thorn (missing one source file), can the model complete it and pass all tests?
 
 **Data Curation Pipeline**:
-1. Collected ~3,000 source files from open-sourced Einstein Toolkit Thorns
-2. Screened for 1,000 problems with runnable tests
-3. Manually verified and selected 40 problems where models are evaluated on physical reasoning abilities, in addition to software engineering skills.
-
-
-### Multi-SWE-Bench Format (msb_type)
-
-244 software engineering tasks from scientific computing repositories:
-- **Sources**: OpenMM, PySCF, RDkit, Qiskit, AMReX, EinsteinToolkit (EisnteinToolkit problems are synthesized. Others are from real issues and pull requests)
-- **Languages**: C++ (\~65%), Python (\~35%)
-- **Tasks**: Bug fixes, feature implementation, code completion
+1. Collected ~3,000 questions from open-sourced Einstein Toolkit Thorns
+2. Screened for 1,085 questions with runnable tests (the questions verified on execution are provided in `et_type`)
+3. Manually verified and selected 40 questions where models are evaluated on physical reasoning abilities, in addition to software engineering skills. (merged to `msb_type`)
 
-Data is formatted following the Multi-SWE-Bench structure with issue descriptions, patches, and test cases.
 
 ## 💻 Usage
 
@@ -104,8 +95,8 @@ from datasets import load_dataset
 dataset = load_dataset("ByteDance-Seed/AInsteinBench")
 
 # Load specific subsets
-et_dataset = load_dataset("ByteDance-Seed/AInsteinBench", "et_type")
 msb_dataset = load_dataset("ByteDance-Seed/AInsteinBench", "msb_type")
+et_dataset = load_dataset("ByteDance-Seed/AInsteinBench", "et_type")
 
 # Access samples
 for sample in dataset['train']:
@@ -128,7 +119,7 @@ The dataset is licensed under CC0, subject to any intellectual property rights i
   - homepage: https://einsteintoolkit.org 
   - arrangement: https://bitbucket.org/einsteintoolkit/einsteintoolkit/
   - license: GPL-2.0
-- Scientific computing repositories: 
+- GitHub repositories: 
   - OpenMM: https://github.com/openmm/openmm (MIT license and the GNU Lesser General Public License )
   - PySCF: https://github.com/pyscf/pyscf (Apache License 2.0)
   - RDkit: https://github.com/rdkit/rdkit (BSD 3-Clause License)