
# 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.
task_categories:text-retrieval task_ids:document-retrieval language_creators:found multilinguality:multilingual source_datasets:original language:code license:cc-by-nc-nd-4.0 arxiv:2306.03091 region:us
{"language_creators": ["found"], "language": ["code"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["multilingual"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "RepoBench-Retrieval"}
TAGS
#task_categories-text-retrieval #task_ids-document-retrieval #language_creators-found #multilinguality-multilingual #source_datasets-original #language-code #license-cc-by-nc-nd-4.0 #arxiv-2306.03091 #region-us 


# Dataset Card for RepoBench-R

## Dataset Description

- Homepage: URL
- Paper: URL

## Dataset Summary

RepoBench-R (Retrieval) is a subtask of RepoBench(GitHub, arXiv), 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:



> 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



## Licensing Information

CC BY-NC-ND 4.0





## Contributions

Thanks to @Leolty for adding this dataset.
# Dataset Card for RepoBench-R ## Dataset Description

- Homepage: URL
- Paper: URL ## Dataset Summary

RepoBench-R (Retrieval) is a subtask of RepoBench(GitHub, arXiv), 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:



> 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 ## Licensing Information

CC BY-NC-ND 4.0 ## Contributions

Thanks to @Leolty for adding this dataset.
TAGS
#task_categories-text-retrieval #task_ids-document-retrieval #language_creators-found #multilinguality-multilingual #source_datasets-original #language-code #license-cc-by-nc-nd-4.0 #arxiv-2306.03091 #region-us 
 # Dataset Card for RepoBench-R ## Dataset Description

- Homepage: URL
- Paper: URL ## Dataset Summary

RepoBench-R (Retrieval) is a subtask of RepoBench(GitHub, arXiv), 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:



> 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 ## Licensing Information

CC BY-NC-ND 4.0 ## Contributions

Thanks to @Leolty for adding this dataset.
passage: TAGS
#task_categories-text-retrieval #task_ids-document-retrieval #language_creators-found #multilinguality-multilingual #source_datasets-original #language-code #license-cc-by-nc-nd-4.0 #arxiv-2306.03091 #region-us 
# Dataset Card for RepoBench-R## Dataset Description

- Homepage: URL
- Paper: URL## Dataset Summary

RepoBench-R (Retrieval) is a subtask of RepoBench(GitHub, arXiv), 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.