README.md

---
license: apache-2.0
task_categories:
- text-generation
- text2text-generation
- translation
language:
- en
tags:
- code
pretty_name: BabelCode TP3
size_categories:
- 1K<n<10K
---
# Dataset Card for Translating Python Programming Puzzles (TP3)

## 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

To quickly evaluate TP3 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 Translating Python Programming Puzzles (TP3) dataset is created from the verification functions in the [Python Programming Puzzles dataset (Schuster et al., 2021)](https://github.com/microsoft/PythonProgrammingPuzzles) to create this dataset. These functions are hand-crafted by the
authors and are used to check if an answer satisfies the constraints of the puzzle. These puzzles range in difficulty from basic character checking to competitive programming problems.

### Supported Tasks and Leaderboards

### Languages
BC-TP3 supports:
* C++
* C#
* Dart
* Go
* Haskell
* Java
* Javascript
* Julia
* Kotlin
* Lua
* PHP
* R
* Rust
* Scala
* TypeScript

## Dataset Structure

```python
>>> from datasets import load_dataset
>>> load_dataset("gabeorlanski/bc-tp3")
DatasetDict({
    test: Dataset({
        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code'],
        num_rows: 2576
    })
})
```

### 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.
- `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.  
- `source`: The source solution in Python.

## Dataset Creation
See section 2 of the [BabelCode Paper](https://arxiv.org/abs/2302.01973) to learn more about how the datasets are translated. 

### Curation Rationale

### Source Data

#### Initial Data Collection and Normalization

#### Who are the source language producers?

### Annotations

#### Annotation process

#### Who are the annotators?

### Personal and Sensitive Information
None.

## Considerations for Using the Data

### Social Impact of Dataset


### Discussion of Biases

### Other Known Limitations

## Additional Information

### 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}
}
@inproceedings{
    schuster2021programming,
    title={Programming Puzzles},
    author={Tal Schuster and Ashwin Kalyan and Alex Polozov and Adam Tauman Kalai},
    booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
    year={2021},
    url={https://arxiv.org/abs/2106.05784}
}
```