--- language_creators: - found language: - code license: - cc-by-nc-nd-4.0 multilinguality: - multilingual pretty_name: RepoBench-Retrieval source_datasets: - original task_categories: - text-retrieval task_ids: - document-retrieval --- # Dataset Card for RepoBench-R ## Table of Contents - [Dataset Card for RepoBench-R](#dataset-card-for-repobench-r) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Dataset Structure](#dataset-structure) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/Leolty/repobench - **Paper:** https://arxiv.org/abs/2306.03091 ### Dataset Summary RepoBench-R is a subtask of [RepoBench](https://github.com/Leolty/repobench), targeting the retrieval component of a repository-level auto-completion system, focusing on extracting the most relevant code snippet from a project repository for next-line code prediction. ### Supported Tasks The dataset supports two programming languages, Python and Java, and contains two settings: - `cff`: short for `cross_file_first`, where the cross-file module in next line is first used in the current file. - `cfr`: short for `cross_file_random`, where the cross-file module in next line is not first used in the current file. For each setting, we provide two levels of difficulty: `easy` and `hard`. Suppose the number of code snippets in the context is k, then for the `easy` subset, we have 5 \leq k < 10, while for the `hard` subset, we have k \geq 10. So, the specific information of each split is as follows:
subset split #
python_cff easy 8,000
hard 8,000
python_cfr easy 4,000
hard 4,000
java_cff easy 8,000
hard 8,000
java_cfr easy 4,000
hard 4,000
## Dataset Structure ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/) for adding this dataset.