metadata

dataset_info:
  features:
    - name: file
      dtype: string
    - name: content
      dtype: string
  splits:
    - name: train
      num_bytes: 243796785.32967034
      num_examples: 990
  download_size: 43230285
  dataset_size: 243796785.32967034
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: mit
task_categories:
  - text-generation
pretty_name: OpenAPI Completion Refined

Dataset Card for OpenAPI Completion Refined

A human-refined dataset of OpenAPI definitions based on the APIs.guru OpenAPI directory. The dataset was used to fine-tune Code Llama for OpenAPI completion in the "Optimizing Large Language Models for OpenAPI Code Completion " paper.

Dataset Details

Dataset Description

The dataset was collected from the APIs.guru OpenAPI definitions directory. The directory contains more than 4,000 definitions in yaml format. Analysis of the repository revealed that about 75% of the definitions in the directory are produced by a handful of major companies like Amazon, Google, and Microsoft. To avoid the dataset bias towards a specific producer, the maximum number of definitions from a single producer was limited to 20. Multiple versions of the same API were also excluded from the dataset as they are likely to contain very similar definitions.

Curated by: Bohdan Petryshyn
Language(s) (NLP): OpenAPI
License: MIT

Dataset Sources

Repository: https://github.com/BohdanPetryshyn/code-llama-fim-fine-tuning
Paper: https://arxiv.org/abs/2405.15729

Citation

If you found the dataset or the fine-tuning code helpful, please reference the original paper:

BibTeX:

@misc{petryshyn2024optimizing,
      title={Optimizing Large Language Models for OpenAPI Code Completion}, 
      author={Bohdan Petryshyn and Mantas Lukoševičius},
      year={2024},
      eprint={2405.15729},
      archivePrefix={arXiv},
      primaryClass={id='cs.SE' full_name='Software Engineering' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.'}
}

APA:

Petryshyn, B., & Lukoševičius, M. (2024). Optimizing Large Language Models for OpenAPI Code Completion. arXiv preprint arXiv:2405.15729.