Datasets:
Tasks:
Text2Text Generation
Languages:
code
Multilinguality:
monolingual
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
| 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} | |
| } | |
| ``` |