
# 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.
task_categories:text-generation task_ids:document-retrieval language_creators:found multilinguality:multilingual size_categories:100K<n<1M source_datasets:original license:cc-by-nc-nd-4.0 code arxiv:2306.03091 region:us
{"language_creators": ["found"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["document-retrieval"], "pretty_name": "RepoBench-Completion", "tags": ["code"]}
TAGS
#task_categories-text-generation #task_ids-document-retrieval #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #license-cc-by-nc-nd-4.0 #code #arxiv-2306.03091 #region-us 


# Dataset Card for RepoBench-C

## Dataset Description

- Homepage: URL
- Paper: URL

## Dataset Summary

RepoBench-C (Completion) is a subtask of RepoBench(GitHub, arXiv), 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: 



> Note: The 'split' argument is optional. If not provided, the entire dataset 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-C ## Dataset Description

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

RepoBench-C (Completion) is a subtask of RepoBench(GitHub, arXiv), 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: 



> Note: The 'split' argument is optional. If not provided, the entire dataset 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-generation #task_ids-document-retrieval #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #license-cc-by-nc-nd-4.0 #code #arxiv-2306.03091 #region-us 
 # Dataset Card for RepoBench-C ## Dataset Description

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

RepoBench-C (Completion) is a subtask of RepoBench(GitHub, arXiv), 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: 



> Note: The 'split' argument is optional. If not provided, the entire dataset will be loaded. ## Dataset Structure ## Licensing Information

CC BY-NC-ND 4.0 ## Contributions

Thanks to @Leolty for adding this dataset.