--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - mit multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - text2text-generation task_ids: [] pretty_name: DocPrompting-CoNaLa tags: - code-generation - doc retrieval - retrieval augmented generation --- ## Dataset Description - **Repository:** https://github.com/shuyanzhou/docprompting - **Paper:** [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/pdf/2207.05987.pdf) ### Dataset Summary This is the re-split of [CoNaLa](https://conala-corpus.github.io/) 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 ```python 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-y`where `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](https://arxiv.org/pdf/1805.08949.pdf) ### 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} } ```