Datasets:
Tasks:
Text Retrieval
Sub-tasks:
document-retrieval
Languages:
code
Multilinguality:
multilingual
Language Creators:
found
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 2,992 Bytes
6c9c30d f304937 391f4b2 6ec8105 3a37514 f304937 6c9c30d 71998d6 f304937 71998d6 f304937 71998d6 514d49e 71998d6 b10acdb f304937 71998d6 514d49e 71998d6 514d49e 71998d6 514d49e 71998d6 514d49e 71998d6 514d49e 71998d6 23c05e6 71998d6 514d49e 71998d6 e3c387a 23c05e6 71998d6 514d49e 71998d6 23c05e6 71998d6 514d49e 71998d6 578c19d 514d49e 71998d6 578c19d 514d49e 71998d6 8fd6e57 514d49e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
---
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
## Dataset Description
- **Homepage:** https://github.com/Leolty/repobench
- **Paper:** https://arxiv.org/abs/2306.03091
## Dataset Summary
**RepoBench-R (Retrieval)** is a subtask of **RepoBench**([GitHub](https://github.com/Leolty/repobench), [arXiv](https://arxiv.org/abs/2306.03091)), targeting the retrieval component of a repository-level auto-completion system, focusing on retrieving the most relevant code snippet from a project repository for next-line
code prediction.
## 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.
## Supported Tasks
The dataset has 4 subsets:
- `python_cff`: python dataset with `cff` setting.
- `python_cfr`: python dataset with `cfr` setting.
- `java_cff`: java dataset with `cff` setting.
- `java_cfr`: java dataset with `cfr` setting.
Each subset has 4 splits:
- `train_easy`: training set with easy difficulty, where the number of code snippets in the context \\(k\\) satisfies \\( 5 \leq k < 10 \\).
- `train_hard`: training set with hard difficulty, where the number of code snippets in the context \\(k\\) satisfies \\( k \geq 10 \\).
- `test_easy`: testing set with easy difficulty.
- `test_hard`: testing set with hard difficulty.
## Loading Data
For example, if you want to load the `test` `cross_file_first` `python` dataset with `easy` difficulty, you can use the following code:
```python
from datasets import load_dataset
dataset = load_dataset("tianyang/repobench-r", "python_cff", split="test_easy")
```
> Note: The `split` argument is optional. If not provided, the entire dataset (including, train and test data with easy and hard level) will be loaded.
## Dataset Structure
```json
{
"repo_name": "repository name of the data point",
"file_path": "path/to/file",
"context": [
"snippet 1",
"snippet 2",
// ...
"snippet k"
],
"import_statement": "all import statements in the file",
"gold_snippet_idex": 2, // the index of the gold snippet in the context list, 0~k-1
"code": "the code for next-line prediction",
"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. |