Dataset Description

• Repository: https://github.com/shuyanzhou/docprompting
• Paper: DocPrompting: Generating Code by Retrieving the Docs

Dataset Summary

This is the re-split of CoNaLa dataset. For each code snippet in the dev and test set, at least one function is held out from the training set. This split aims at testing a code generation model's capacity in generating unseen functions We further make sure that examples from the same StackOverflow post (same question_id before -) are in the same split.

Supported Tasks and Leaderboards

This dataset is used to evaluate code generations.

Languages

English - Python code.

Dataset Structure

dataset = load_dataset("neulab/docpromting-conala")
DatasetDict({
    train: Dataset({
        features: ['nl', 'cmd', 'question_id', 'cmd_name', 'oracle_man', 'canonical_cmd'],
        num_rows: 2135
    })
    test: Dataset({
        features: ['nl', 'cmd', 'question_id', 'cmd_name', 'oracle_man', 'canonical_cmd'],
        num_rows: 543
    })
    validation: Dataset({
        features: ['nl', 'cmd', 'question_id', 'cmd_name', 'oracle_man', 'canonical_cmd'],
        num_rows: 201
    })
})
})

code_docs = load_dataset("neulab/docprompting-conala", "docs")
DatasetDict({
    train: Dataset({
        features: ['doc_id', 'doc_content'],
        num_rows: 34003
    })
})

Data Fields

train/dev/test:

• nl: The natural language intent
• cmd: The reference code snippet
• question_id: x-ywhere x is the StackOverflow post ID
• oracle_man: The doc_id of the functions used in the reference code snippet. The corresponding contents are in doc split
• canonical_cmd: The canonical version reference code snippet

docs:

• doc_id: the id of a doc
• doc_content: the content of the doc

Dataset Creation

The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper

Citation Information

@article{zhou2022doccoder,
  title={DocCoder: Generating Code by Retrieving and Reading Docs},
  author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and JIang, Zhengbao and Neubig, Graham},
  journal={arXiv preprint arXiv:2207.05987},
  year={2022}
}