Datasets:
mbpp

	---
	annotations_creators:
	- crowdsourced
	- expert-generated
	language_creators:
	- crowdsourced
	- expert-generated
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- n<1K
	source_datasets:
	- original
	task_categories:
	- text2text-generation
	task_ids: []
	pretty_name: Mostly Basic Python Problems
	tags:
	- code-generation
	dataset_info:
	- config_name: full
	features:
	- name: task_id
	dtype: int32
	- name: text
	dtype: string
	- name: code
	dtype: string
	- name: test_list
	sequence: string
	- name: test_setup_code
	dtype: string
	- name: challenge_test_list
	sequence: string
	splits:
	- name: train
	num_bytes: 176879
	num_examples: 374
	- name: test
	num_bytes: 244104
	num_examples: 500
	- name: validation
	num_bytes: 42405
	num_examples: 90
	- name: prompt
	num_bytes: 4550
	num_examples: 10
	download_size: 236069
	dataset_size: 467938
	- config_name: sanitized
	features:
	- name: source_file
	dtype: string
	- name: task_id
	dtype: int32
	- name: prompt
	dtype: string
	- name: code
	dtype: string
	- name: test_imports
	sequence: string
	- name: test_list
	sequence: string
	splits:
	- name: train
	num_bytes: 63453
	num_examples: 120
	- name: test
	num_bytes: 132720
	num_examples: 257
	- name: validation
	num_bytes: 20050
	num_examples: 43
	- name: prompt
	num_bytes: 3407
	num_examples: 7
	download_size: 115422
	dataset_size: 219630
	configs:
	- config_name: full
	data_files:
	- split: train
	path: full/train-*
	- split: test
	path: full/test-*
	- split: validation
	path: full/validation-*
	- split: prompt
	path: full/prompt-*
	default: true
	- config_name: sanitized
	data_files:
	- split: train
	path: sanitized/train-*
	- split: test
	path: sanitized/test-*
	- split: validation
	path: sanitized/validation-*
	- split: prompt
	path: sanitized/prompt-*
	---

	# Dataset Card for Mostly Basic Python Problems (mbpp)

	## Table of Contents
	- [Dataset Card for Mostly Basic Python Problems (mbpp)](#dataset-card-for-mostly-basic-python-problems-(mbpp))
	- [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)
	- [Source Data](#source-data)
	- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
	- [Who are the source language producers?](#who-are-the-source-language-producers)
	- [Annotations](#annotations)
	- [Annotation process](#annotation-process)
	- [Who are the annotators?](#who-are-the-annotators)
	- [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)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description
	- Repository: https://github.com/google-research/google-research/tree/master/mbpp
	- Paper: [Program Synthesis with Large Language Models](https://arxiv.org/abs/2108.07732)

	### Dataset Summary
	The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us.

	Released [here](https://github.com/google-research/google-research/tree/master/mbpp) as part of [Program Synthesis with Large Language Models, Austin et. al., 2021](https://arxiv.org/abs/2108.07732).

	### Supported Tasks and Leaderboards
	This dataset is used to evaluate code generations.

	### Languages
	English - Python code

	## Dataset Structure

	```python
	dataset_full = load_dataset("mbpp")
	DatasetDict({
	test: Dataset({
	features: ['task_id', 'text', 'code', 'test_list', 'test_setup_code', 'challenge_test_list'],
	num_rows: 974
	})
	})

	dataset_sanitized = load_dataset("mbpp", "sanitized")
	DatasetDict({
	test: Dataset({
	features: ['source_file', 'task_id', 'prompt', 'code', 'test_imports', 'test_list'],
	num_rows: 427
	})
	})
	```

	### Data Instances

	#### mbpp - full
	```
	{
	'task_id': 1,
	'text': 'Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].',
	'code': 'R = 3\r\nC = 3\r\ndef min_cost(cost, m, n): \r\n\ttc = [[0 for x in range(C)] for x in range(R)] \r\n\ttc[0][0] = cost[0][0] \r\n\tfor i in range(1, m+1): \r\n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \r\n\tfor j in range(1, n+1): \r\n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \r\n\tfor i in range(1, m+1): \r\n\t\tfor j in range(1, n+1): \r\n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \r\n\treturn tc[m][n]',
	'test_list': [
	'assert min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8',
	'assert min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12',
	'assert min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16'],
	'test_setup_code': '',
	'challenge_test_list': []
	}
	```
	#### mbpp - sanitized
	```
	{
	'source_file': 'Benchmark Questions Verification V2.ipynb',
	'task_id': 2,
	'prompt': 'Write a function to find the shared elements from the given two lists.',
	'code': 'def similar_elements(test_tup1, test_tup2):\n res = tuple(set(test_tup1) & set(test_tup2))\n return (res) ',
	'test_imports': [],
	'test_list': [
	'assert set(similar_elements((3, 4, 5, 6),(5, 7, 4, 10))) == set((4, 5))',
	'assert set(similar_elements((1, 2, 3, 4),(5, 4, 3, 7))) == set((3, 4))',
	'assert set(similar_elements((11, 12, 14, 13),(17, 15, 14, 13))) == set((13, 14))'
	]
	}
	```
	### Data Fields

	- `source_file`: unknown
	- `text`/`prompt`: description of programming task
	- `code`: solution for programming task
	- `test_setup_code`/`test_imports`: necessary code imports to execute tests
	- `test_list`: list of tests to verify solution
	- `challenge_test_list`: list of more challenging test to further probe solution

	### Data Splits
	There are two version of the dataset (full and sanitized), each with four splits:
	- train
	- evaluation
	- test
	- prompt

	The `prompt` split corresponds to samples used for few-shot prompting and not for training.

	## Dataset Creation
	See section 2.1 of original [paper](https://arxiv.org/abs/2108.07732).

	### Curation Rationale
	In order to evaluate code generation functions a set of simple programming tasks as well as solutions is necessary which this dataset provides.

	### Source Data

	#### Initial Data Collection and Normalization
	The dataset was manually created from scratch.

	#### Who are the source language producers?
	The dataset was created with an internal crowdsourcing effort at Google.

	### Annotations

	#### Annotation process
	The full dataset was created first and a subset then underwent a second round to improve the task descriptions.

	#### Who are the annotators?
	The dataset was created with an internal crowdsourcing effort at Google.

	### Personal and Sensitive Information
	None.

	## Considerations for Using the Data
	Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful.

	### Social Impact of Dataset
	With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.

	### Discussion of Biases

	### Other Known Limitations
	Since the task descriptions might not be expressive enough to solve the task. The `sanitized` split aims at addressing this issue by having a second round of annotators improve the dataset.

	## Additional Information

	### Dataset Curators
	Google Research

	### Licensing Information
	CC-BY-4.0

	### Citation Information
	```
	@article{austin2021program,
	title={Program Synthesis with Large Language Models},
	author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others},
	journal={arXiv preprint arXiv:2108.07732},
	year={2021}
	```
	### Contributions
	Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.