Datasets:
Tasks:
Text2Text Generation
Languages:
code
Multilinguality:
monolingual
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
File size: 1,747 Bytes
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---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: OpenAI HumanEval-Infilling
tags:
- code-generation
---
# HumanEval-Infilling
## Dataset Description
- **Repository:** https://github.com/openai/human-eval-infilling
- **Paper:** https://arxiv.org/pdf/2207.14255
## Dataset Summary
[HumanEval-Infilling](https://github.com/openai/human-eval-infilling) is a benchmark for infilling tasks, derived from [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark for the evaluation of code generation models.
## Dataset Structure
To load the dataset you need to specify a subset. By default `HumanEval-SingleLineInfilling` is loaded.
```python
from datasets import load_dataset
ds = load_dataset("humaneval_infilling", "HumanEval-RandomSpanInfilling")
DatasetDict({
test: Dataset({
features: ['task_id', 'entry_point', 'prompt', 'suffix', 'canonical_solution', 'test'],
num_rows: 1640
})
})
```
## Subsets
This dataset has 4 subsets: HumanEval-MultiLineInfilling, HumanEval-SingleLineInfilling, HumanEval-RandomSpanInfilling, HumanEval-RandomSpanInfillingLight.
The single-line, multi-line, random span infilling and its light version have 1033, 5815, 1640 and 164 tasks, respectively.
## Citation
```
@article{bavarian2022efficient,
title={Efficient Training of Language Models to Fill in the Middle},
author={Bavarian, Mohammad and Jun, Heewoo and Tezak, Nikolas and Schulman, John and McLeavey, Christine and Tworek, Jerry and Chen, Mark},
journal={arXiv preprint arXiv:2207.14255},
year={2022}
}
``` |