Create README.md

README.md

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----
-license: apache-2.0
----
+---
+dataset_info:
+  features:
+  - name: task_id
+    dtype: string
+  - name: language
+    dtype: string
+  - name: prompt
+    dtype: string
+  - name: test
+    dtype: string
+  - name: entry_point
+    dtype: string
+  splits:
+  - name: multilingual-humaneval_python
+    num_bytes: 165716
+    num_examples: 164
+  download_size: 67983
+  dataset_size: 165716
+license: apache-2.0
+task_categories:
+- text-generation
+tags:
+- mxeval
+- code-generation
+- mbxp
+- multi-humaneval
+- mathqax
+pretty_name: mxeval
+language:
+- en
+---
+# MxEval
+**M**ultilingual E**x**ecution **Eval**uation
+
+## Table of Contents
+- [MxEval](#MxEval)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Executional Correctness](#execution)
+    - [Execution Example](#execution-example)
+    - [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** [GitHub Repository](https://github.com/amazon-science/mbxp-exec-eval)
+- **Paper:** [Multi-lingual Evaluation of Code Generation Models](https://openreview.net/forum?id=Bo7eeXm6An8)
+
+### Dataset Summary
+
+This repository contains data and code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data,
+namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval.
+<br>Results and findings can be found in the paper ["Multi-lingual Evaluation of Code Generation Models"](https://arxiv.org/abs/2210.14868).
+
+
+### Supported Tasks and Leaderboards
+* [MBXP](https://huggingface.co/datasets/mxeval/mbxp)
+* [Multi-HumanEval](https://huggingface.co/datasets/mxeval/multi-humaneval)
+* [MathQA-X](https://huggingface.co/datasets/mxeval/mathqa-x)
+
+### Languages
+The programming problems are written in multiple programming languages and contain English natural text in comments and docstrings.
+
+
+## Dataset Structure
+To lookup currently supported datasets
+```python
+get_dataset_config_names("mxeval/mxeval")
+['mathqa-x', 'mbxp', 'multi-humaneval']
+```
+To load a specific dataset and language
+```python
+from datasets import load_dataset
+load_dataset("mxeval/mxeval", "mbxp", split="python")
+Dataset({
+    features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'description', 'canonical_solution'],
+    num_rows: 974
+})
+```
+
+### Data Instances
+
+An example of a dataset instance:
+
+```python
+{
+  "task_id": "MBSCP/6",
+  "language": "scala",
+  "prompt": "object Main extends App {\n    /**\n     * You are an expert Scala programmer, and here is your task.\n     * * Write a Scala function to check whether the two numbers differ at one bit position only or not.\n     *\n     * >>> differAtOneBitPos(13, 9)\n     * true\n     * >>> differAtOneBitPos(15, 8)\n     * false\n     * >>> differAtOneBitPos(2, 4)\n     * false\n     */\n    def differAtOneBitPos(a : Int, b : Int) : Boolean = {\n",
+  "test": "\n\n    var arg00 : Int = 13\n    var arg01 : Int = 9\n    var x0 : Boolean = differAtOneBitPos(arg00, arg01)\n    var v0 : Boolean = true\n    assert(x0 == v0, \"Exception -- test case 0 did not pass. x0 = \" + x0)\n\n    var arg10 : Int = 15\n    var arg11 : Int = 8\n    var x1 : Boolean = differAtOneBitPos(arg10, arg11)\n    var v1 : Boolean = false\n    assert(x1 == v1, \"Exception -- test case 1 did not pass. x1 = \" + x1)\n\n    var arg20 : Int = 2\n    var arg21 : Int = 4\n    var x2 : Boolean = differAtOneBitPos(arg20, arg21)\n    var v2 : Boolean = false\n    assert(x2 == v2, \"Exception -- test case 2 did not pass. x2 = \" + x2)\n\n\n}\n",
+  "entry_point": "differAtOneBitPos",
+  "description": "Write a Scala function to check whether the two numbers differ at one bit position only or not."
+}
+```
+
+### Data Fields
+
+- `task_id`: identifier for the data sample
+- `prompt`: input for the model containing function header and docstrings
+- `canonical_solution`: solution for the problem in the `prompt`
+- `description`: task description
+- `test`: contains function to test generated code for correctness
+- `entry_point`: entry point for test
+- `language`: programming lanuage identifier to call the appropriate subprocess call for program execution
+
+
+### Data Splits
+
+ - HumanXEval
+   - Python 
+   - Java 
+   - JavaScript
+   - Csharp
+   - CPP
+   - Go
+   - Kotlin
+   - PHP
+   - Perl
+   - Ruby
+   - Swift
+   - Scala
+ - MBXP
+   - Python
+   - Java
+   - JavaScript
+   - TypeScript
+   - Csharp
+   - CPP
+   - Go
+   - Kotlin
+   - PHP
+   - Perl
+   - Ruby
+   - Swift
+   - Scala
+ - MathQA
+   - Python
+   - Java
+   - JavaScript
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps.
+
+### Personal and Sensitive Information
+
+None.
+
+### Social Impact of Dataset
+With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
+
+### Dataset Curators
+AWS AI Labs
+
+## 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
+>>> mbxp_python = load_dataset("mxeval/mxeval", "mbxp", split="python")
+>>> example_problem = mbxp_python[0]
+>>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0)
+{'task_id': 'MBPP/1', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 10.582208633422852}
+```
+### Considerations for Using the Data
+Make sure to sandbox the execution environment since generated code samples can be harmful.
+
+
+### Licensing Information
+
+[LICENSE](https://huggingface.co/datasets/mxeval/mxeval/blob/main/LICENSE) <br>
+[THIRD PARTY LICENSES](https://huggingface.co/datasets/mxeval/mxeval/blob/main/THIRD_PARTY_LICENSES)
+
+# Citation Information
+```
+@article{mbxp_athiwaratkun2022,
+  title = {Multi-lingual Evaluation of Code Generation Models},
+  author = {Athiwaratkun, Ben and
+   Gouda, Sanjay Krishna and
+   Wang, Zijian and
+   Li, Xiaopeng and
+   Tian, Yuchen and
+   Tan, Ming
+   and Ahmad, Wasi Uddin and
+   Wang, Shiqi and
+   Sun, Qing and
+   Shang, Mingyue and
+   Gonugondla, Sujan Kumar and
+   Ding, Hantian and
+   Kumar, Varun and
+   Fulton, Nathan and
+   Farahani, Arash and
+   Jain, Siddhartha and
+   Giaquinto, Robert and
+   Qian, Haifeng and
+   Ramanathan, Murali Krishna and
+   Nallapati, Ramesh and
+   Ray, Baishakhi and
+   Bhatia, Parminder and
+   Sengupta, Sudipta and
+   Roth, Dan and
+   Xiang, Bing},
+  doi = {10.48550/ARXIV.2210.14868},
+  url = {https://arxiv.org/abs/2210.14868},
+  keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+# Contributions
+
+[skgouda@](https://github.com/sk-g) [benathi@](https://github.com/benathi)