README.md

---
pretty_name: HumanEval-no-resource
license: mit
language:
- en
task_categories:
- text-generation
tags:
- benchmark
- evaluation
- multilingual
- code-generation
- low-resource
- no-resource
dataset_info:
  features:
  - name: task_id
    dtype: int64
  - name: original_humaneval_id
    dtype: string
  - name: prompt
    dtype: string
  - name: tests
    dtype: string
  splits:
  - name: gleam
    num_bytes: 372702
    num_examples: 154
  - name: moonbit
    num_bytes: 133847
    num_examples: 154
  download_size: 158902
  dataset_size: 506549
configs:
- config_name: default
  data_files:
  - split: gleam
    path: data/gleam-*
  - split: moonbit
    path: data/moonbit-*
---


# HumanEval Benchmark for Gleam and MoonBit

## Dataset Description
This dataset contains a translation of the **HumanEval** benchmark into two no-resource programming languages: **Gleam** and **MoonBit**.

This dataset is derived from the [**MultiPL-E**](https://huggingface.co/datasets/nuprl/MultiPL-E) benchmark, which extends HumanEval by translating its programming tasks into multiple programming languages. 

More details on this benchmark and how it was created can be found in the paper [No Resource, No Benchmarks, No Problem? Evaluating and Improving LLMs for Code Generation in No-Resource Languages](https://huggingface.co/papers/2606.16827).

## Dataset Statistics

- Number of tasks: **154**
- Programming languages: **2**
- Total examples: **308**
- Source benchmark: **HumanEval**
- Intermediate benchmark: **MultiPL-E**

## Supported Languages

- Gleam
- MoonBit

## Dataset Structure

Each example contains the following fields:

| Field | Description |
|-------|-------------|
| `task_id` | Identifier of the translated task in this benchmark. |
| `original_humaneval_id` | Identifier of the task in the original HumanEval benchmark. |
| `prompt` | Signature and docstring of the function to be implemented. |
| `tests` | Test suite used to evaluate the correctness of generated solutions. |

## Usage
To load this benchmark, you can use the `datasets` library as follows.

First, install the library if you haven't already:

```bash
pip install datasets
```

Then, load the dataset:

```python
from datasets import load_dataset

dataset = load_dataset("Devy1/humaneval-no-resource")

# Load the Gleam split
gleam_split = dataset["gleam"]
print(gleam_split[0])
```

Or load only a specific language:

```python
gleam_ds = load_dataset("Devy1/humaneval-no-resource", split="gleam")
```

> **Note:** Executing the test suites requires the corresponding language environments and dependencies. The complete generation and evaluation pipeline, including setup instructions for Gleam and MoonBit, is available in our [GitHub repository](https://github.com/Devy99/no-resource-pl-study).

## Citation

If you use this dataset, please cite our work:

```bibtex
@article{giagnorio2026noresource,
  title={No Resource, No Benchmarks, No Problem? Evaluating and Improving LLMs for Code Generation in No-Resource Languages},
  author={Giagnorio, Alessandro and Martin-Lopez, Alberto and Bavota, Gabriele},
  journal={IEEE Transactions on Software Engineering},
  year={2026},
  publisher={IEEE}
}
```

Since this dataset is derived from HumanEval through MultiPL-E, please also cite the original benchmark papers:

```bibtex
@article{chen2021evaluating,
  title={Evaluating large language models trained on code},
  author={Chen, Mark and Tworek, Jerry and Jun, Heewoo and Yuan, Qiming and Pinto, Henrique Ponde De Oliveira and Kaplan, Jared and Edwards, Harri and Burda, Yuri and Joseph, Nicholas and Brockman, Greg and others},
  journal={arXiv preprint arXiv:2107.03374},
  year={2021}
}
```

```bibtex
@article{cassano2023multipl,
  title={Multipl-e: A scalable and polyglot approach to benchmarking neural code generation},
  author={Cassano, Federico and Gouwar, John and Nguyen, Daniel and Nguyen, Sydney and Phipps-Costin, Luna and Pinckney, Donald and Yee, Ming-Ho and Zi, Yangtian and Anderson, Carolyn Jane and Feldman, Molly Q and others},
  journal={IEEE Transactions on Software Engineering},
  volume={49},
  number={7},
  pages={3675--3691},
  year={2023},
  publisher={IEEE}
}
```

### Paper

* Hugging Face Papers: [https://huggingface.co/papers/2606.16827](https://huggingface.co/papers/2606.16827)