---
license: apache-2.0
task_categories:
- text-generation
- text2text-generation
language:
- en
tags:
- code
pretty_name: BabelCode MBPP
size_categories:
- 1K<n<10K
---
# Dataset Card for BabelCode MBPP

## Dataset Description

- **Repository:** [GitHub Repository](https://github.com/google-research/babelcode)
- **Paper:** [Measuring The Impact Of Programming Language Distribution](https://arxiv.org/abs/2302.01973)

### How To Use This Dataset

First follow the install instructions for [BabelCode](https://github.com/google-research/babelcode).

To quickly evaluate BC-MBPP predictions, save the `qid` and `language` keys along with the postprocessed prediction code in a JSON lines file. Then follow the install instructions for [BabelCode](https://github.com/google-research/babelcode), and you can evaluate your predictions. 

### Dataset Summary

The BabelCode-MBPP (BC-MBPP) dataset converts the [MBPP dataset released by Google](https://arxiv.org/abs/2108.07732) to 16 programming languages.

### Supported Tasks and Leaderboards

### Languages
BC-MBPP supports:
* C++
* C#
* Dart
* Go
* Haskell
* Java
* Javascript
* Julia
* Kotlin
* Lua
* PHP
* Python
* R
* Rust
* Scala
* TypeScript

## Dataset Structure

```python
>>> from datasets import load_dataset
>>> load_dataset("gabeorlanski/bc-mbpp")
DatasetDict({
    train: Dataset({
        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution', 'test_list', 'test_case_ids'],
        num_rows: 5308
    })
    test: Dataset({
        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution', 'test_list', 'test_case_ids'],
        num_rows: 6989
    })
    validation: Dataset({
        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution', 'test_list', 'test_case_ids'],
        num_rows: 1216
    })
    prompt: Dataset({
        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution', 'test_list', 'test_case_ids'],
        num_rows: 160
    })
})
```

### Data Fields

- `qid`: The question ID used for running tests.
- `title`: The title of the question.
- `language`: The programming language of the example.
- `text`: The description of the problem.
- `signature`: The signature for the problem.
- `signature_with_docstring`: The signature with the adequately formatted docstring for the given problem.
- `arguments`: The arguments of the problem.
- `entry_fn_name`: The function's name to use an entry point.
- `entry_cls_name`: The class name to use an entry point.
- `solution`: The solution in Python.
- `test_code`: The raw testing script used in the language. If you want to use this, replace `PLACEHOLDER_FN_NAME` (and `PLACEHOLDER_CLS_NAME` if needed) with the corresponding entry points. Next, replace `PLACEHOLDER_CODE_BODY` with the postprocessed prediction.  
- `test_list`: The raw json line of the list of tests for the problem. To load them, use `json.loads`
- `test_case_ids`: The list of test case ids for the problem. These are used to determine if a prediction passes or not.
## Dataset Creation
See section 2 of the [BabelCode Paper](https://arxiv.org/abs/2302.01973) to learn more about how the datasets are translated. 

Information on how the original MBPP was curated is located [here](https://huggingface.co/datasets/mbpp). 

### Dataset Curators
Google Research

### Licensing Information
CC-BY-4.0

### Citation Information
```
@article{orlanski2023measuring,
  title={Measuring The Impact Of Programming Language Distribution},
  author={Orlanski, Gabriel and Xiao, Kefan and Garcia, Xavier and Hui, Jeffrey and Howland, Joshua and Malmaud, Jonathan and Austin, Jacob and Singh, Rishah and Catasta, Michele},
  journal={arXiv preprint arXiv:2302.01973},
  year={2023}
}
@article{Austin2021ProgramSW,
  title={Program Synthesis with Large Language Models},
  author={Jacob Austin and Augustus Odena and Maxwell Nye and Maarten Bosma and Henryk Michalewski and David Dohan and Ellen Jiang and Carrie J. Cai and Michael Terry and Quoc V. Le and Charles Sutton},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.07732}
}
```