Datasets:

mxeval
/

multi-humaneval

	---
	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: multi-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
	- multi-humaneval
	- humaneval
	pretty_name: multi-humaneval
	language:
	- en
	---
	# Multi-HumanEval

	## Table of Contents
	- [multi-humaneval](#multi-humaneval)
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#related-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [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)
	- [Social Impact of Dataset](#social-impact-of-dataset)

	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	# multi-humaneval

	## 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).


	### Related Tasks and Leaderboards
	* [Multi-HumanEval](https://huggingface.co/datasets/mxeval/multi-humaneval)
	* [MBXP](https://huggingface.co/datasets/mxeval/mbxp)
	* [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/multi-humaneval")
	['python', 'csharp', 'go', 'java', 'javascript', 'kotlin', 'perl', 'php', 'ruby', 'scala', 'swift', 'typescript']

	```
	To load a specific dataset and language
	```python
	from datasets import load_dataset
	load_dataset("mxeval/multi-humaneval", "python")
	DatasetDict({
	test: Dataset({
	features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'canonical_solution', 'description'],
	num_rows: 164
	})
	})
	```

	### Data Instances

	An example of a dataset instance:

	```python
	{
	"task_id": "HumanEval/0",
	"language": "python",
	"prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n",
	"test": "\n\nMETADATA = {\n \"author\": \"jt\",\n \"dataset\": \"test\"\n}\n\n\ndef check(candidate):\n assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\n",
	"entry_point": "has_close_elements",
	"canonical_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < threshold:\n return True\n\n return False\n",
	"description": "Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True"
	}
	```

	### 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
	- Csharp
	- Go
	- Java
	- Javascript
	- Kotlin
	- Perl
	- Php
	- Ruby
	- Scala
	- Swift
	- Typescript

	## 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.

	## 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
	>>> humaneval_python = load_dataset("mxeval/multi-humaneval", "python", split="test")
	>>> example_problem = humaneval_python[0]
	>>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0)
	{'task_id': 'HumanEval/0', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 9.636878967285156}
	```

	### Considerations for Using the Data
	Make sure to sandbox the execution environment.

	### Dataset Curators
	AWS AI Labs

	### Licensing Information

	[LICENSE](https://huggingface.co/datasets/mxeval/multi-humaneval/blob/main/multi-humaneval-LICENSE) <br>
	[THIRD PARTY LICENSES](https://huggingface.co/datasets/mxeval/multi-humaneval/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)