Update README.md

README.md

@@ -25,11 +25,14 @@ size_categories:
     - [Data Instances](#data-instances)
     - [Data Fields](#data-fields)
     - [Data Splits](#data-splits)
+  - [Executional Correctness](#execution)
+    - [Execution Example](#execution-example)
+    - [Considerations for Using the Data](#considerations-for-using-the-data)
   - [Dataset Creation](#dataset-creation)
     - [Curation Rationale](#curation-rationale)
     - [Personal and Sensitive Information](#personal-and-sensitive-information)
-  - [Considerations for Using the Data](#considerations-for-using-the-data)
     - [Social Impact of Dataset](#social-impact-of-dataset)
+
   - [Additional Information](#additional-information)
     - [Dataset Curators](#dataset-curators)
     - [Licensing Information](#licensing-information)
@@ -120,12 +123,26 @@ Since code generation models are often trained on dumps of GitHub a dataset not
 
 None.
 
-## Considerations for Using the Data
-Make sure to sandbox the execution environment.
-
 ### Social Impact of Dataset
 With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
 
+## Execution
+
+### Execution Example
+Install the repo [mbxp-exec-eval](https://github.com/amazon-science/mbxp-exec-eval) to execute generations or canonical solutions for the prompts from this dataset.
+
+```python
+>>> from datasets import load_dataset
+>>> from mxeval.execution import check_correctness
+>>> mathqa_python = load_dataset("mxeval/mathqa-x", "python", split="test")
+>>> example_problem = mathqa_python[0]
+>>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0)
+{'task_id': 'MathQA/0', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 9.673357009887695}
+```
+
+### Considerations for Using the Data
+Make sure to sandbox the execution environment.
+
 ### Dataset Curators
 AWS AI Labs