|
--- |
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annotations_creators: |
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- crowdsourced |
|
- expert-generated |
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language_creators: |
|
- crowdsourced |
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- expert-generated |
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language: |
|
- en |
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license: |
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- cc-by-4.0 |
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multilinguality: |
|
- monolingual |
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size_categories: |
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- n<1K |
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source_datasets: |
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- original |
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task_categories: |
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- text2text-generation |
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task_ids: [] |
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pretty_name: Mostly Basic Python Problems |
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tags: |
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- code-generation |
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dataset_info: |
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- config_name: full |
|
features: |
|
- name: task_id |
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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 |
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path: full/train-* |
|
- split: test |
|
path: full/test-* |
|
- split: validation |
|
path: full/validation-* |
|
- split: prompt |
|
path: full/prompt-* |
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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 |
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- [Dataset Card for Mostly Basic Python Problems (mbpp)](#dataset-card-for-mostly-basic-python-problems-(mbpp)) |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) |
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- [Who are the source language producers?](#who-are-the-source-language-producers) |
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- [Annotations](#annotations) |
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- [Annotation process](#annotation-process) |
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- [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) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [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 |
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dataset_full = load_dataset("mbpp") |
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DatasetDict({ |
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test: Dataset({ |
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features: ['task_id', 'text', 'code', 'test_list', 'test_setup_code', 'challenge_test_list'], |
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num_rows: 974 |
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}) |
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}) |
|
|
|
dataset_sanitized = load_dataset("mbpp", "sanitized") |
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DatasetDict({ |
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test: Dataset({ |
|
features: ['source_file', 'task_id', 'prompt', 'code', 'test_imports', 'test_list'], |
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num_rows: 427 |
|
}) |
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}) |
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``` |
|
|
|
### Data Instances |
|
|
|
#### mbpp - full |
|
``` |
|
{ |
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'task_id': 1, |
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'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[][].', |
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'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]', |
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'test_list': [ |
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'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', |
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'assert min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16'], |
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'test_setup_code': '', |
|
'challenge_test_list': [] |
|
} |
|
``` |
|
#### mbpp - sanitized |
|
``` |
|
{ |
|
'source_file': 'Benchmark Questions Verification V2.ipynb', |
|
'task_id': 2, |
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'prompt': 'Write a function to find the shared elements from the given two lists.', |
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'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))' |
|
] |
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} |
|
``` |
|
### 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. |