README.md

---
license: mit
task_categories:
- text-generation
language:
- ja
- en
tags:
- code

configs:
  - config_name: ja
    data_files:
      - split: test
        path:
          - sakuraeval/ja.parquet
  - config_name: en
    data_files:
      - split: test
        path:
          - sakuraeval/en.parquet
---
# SakuraEval

## Dataset Description
SakuraEval is a Japan-specific code generation benchmark dataset. 
It is designed independently and does not rely on translation from English benchmarks such as HumanEval or JHumanEval.

## Dataset Structure
```python
from datasets import load_dataset
load_dataset("kogi-jwu/sakuraeval", "ja")
DatasetDict({
    test: Dataset({
        features: ['task_id', 'category', 'prompt', 'canonical_solution', 'test', 'entry_point'],
        num_rows: 164
    })
})
```

## Data Fields
- task_id: Identifier for the data sample.
- category: Task category.  
- prompt: Input for the model, including the function header and docstring that describes the task.
- canonical_solution: Solution to the problem presented in the prompt.
- test: Function(s) to test the generated code for correctness.
- entry_point: Entry point function to begin testing.

### Category Breakdown
| Category                         | Number of Tasks |
|----------------------------------|-----------------|
| 文化（Culture）                   | 34              |
| 風習（Customs）                   | 27              |
| 日本地理（Japanese Geography）     | 10              |
| 公民・法律（Law and Civics）       | 11              |
| 数学・科学（Math and Science）     | 21              |
| 単位変換（Unit Conversion）        | 11              |
| 日本語処理（Japanese Language）     | 43              |
| その他（Other）                    | 7               |


## Languages
The dataset contains coding problems in 2 natural languages: English and Japanese.