Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
document-retrieval
Size:
100M<n<1B
ArXiv:
License:
| language_creators: | |
| - found | |
| license: | |
| - cc-by-nc-nd-4.0 | |
| multilinguality: | |
| - multilingual | |
| pretty_name: RepoBench-Completion | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-generation | |
| task_ids: | |
| - document-retrieval | |
| tags: | |
| - code | |
| size_categories: | |
| - 100K<n<1M | |
| # Dataset Card for RepoBench-C | |
| ## Dataset Description | |
| - **Homepage:** https://github.com/Leolty/repobench | |
| - **Paper:** https://arxiv.org/abs/2306.03091 | |
| ## Dataset Summary | |
| **RepoBench-C (Completion)** is a subtask of **RepoBench**([GitHub](https://github.com/Leolty/repobench), [arXiv](https://arxiv.org/abs/2306.03091)), focuing on the prediction of the next line of code, given in-file context (including several preceding lines and import statements), and cross-file context. | |
| ## Settings | |
| - `cff`: short for cross_file_first, indicating the cross-file module in next line is first used in the current file. | |
| - `cfr`: short for cross_file_random, indicating the cross-file module in next line is not first used in the current file. | |
| - `if`: short for in_file, indicating the next line does not contain any cross-file module. | |
| ## Supported Tasks | |
| - `python_cff`: python code prediction with cross-file-first setting. | |
| - `python_cfr`: python code prediction with cross-file-random setting. | |
| - `python_if`: python code prediction with in-file setting. | |
| - `java_cff`: java code prediction with cross-file-first setting. | |
| - `java_cfr`: java code prediction with cross-file-random setting. | |
| - `java_if`: java code prediction with in-file setting. | |
| ## Loading Data | |
| For example, if you want to load the `test` set to test your model on `Python` code prediction with `cff` setting, you can do the following: | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("tianyang/repobench-c", "python_cff", split="test") | |
| ``` | |
| > Note: The `split` argument is optional. If not provided, the entire dataset will be loaded. | |
| ## Dataset Structure | |
| ```json | |
| { | |
| "repo_name": "repository name of the data point", | |
| "file_path": "path/to/file", | |
| "context": "commented and concatenated cross-file context", | |
| "import_statement": "all import statements in the file", | |
| "code": "the code for next-line prediction", | |
| "prompt": "cross-file context + import statements + in-file code", | |
| "next_line": "the next line of the code" | |
| } | |
| ``` | |
| ## Licensing Information | |
| CC BY-NC-ND 4.0 | |
| ## Citation Information | |
| ```bibtex | |
| @misc{liu2023repobench, | |
| title={RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems}, | |
| author={Tianyang Liu and Canwen Xu and Julian McAuley}, | |
| year={2023}, | |
| eprint={2306.03091}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` | |
| ## Contributions | |
| Thanks to [@Leolty](https://github.com/Leolty) for adding this dataset. |