
## Dataset Description
- **Repository:** https://github.com/shuyanzhou/docprompting
- **Paper:** [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/pdf/2207.05987.pdf)

### Dataset Summary
This is the natural language to bash generation dataset we harvested from the English subset of [`tldr`](https://github.com/tldr-pages/tldr)
We split the dataset by bash commands. Every command in the dev and test set is held out from the training set.

### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations.

### Languages
English - Bash

## Dataset Structure
```python
dataset = load_dataset("neulab/tldr")
DatasetDict({
    train: Dataset({
        features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
        num_rows: 6414
    })
    test: Dataset({
        features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
        num_rows: 928
    })
    validation: Dataset({
        features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
        num_rows: 1845
    })
})

code_docs = load_dataset("neulab/docprompting-conala", "docs")
DatasetDict({
    train: Dataset({
        features: ['doc_id', 'doc_content'],
        num_rows: 439064
    })
})
```

### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: the unique id of a question
- oracle_man: The `doc_id` of the functions used in the reference code snippet. The corresponding contents are in `doc` split
- cmd_name: the bash command of this code snippet
- tldr_cmd_name: the bash command used in tldr github repo. The `cmd_name` and `tldr_cmd_name` can be different due to naming difference
- manual_exist: whether the manual exists in https://manned.org
- matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token.
 

docs:
- doc_id: the id of a doc
- doc_content: the content of the doc

## Dataset Creation
The dataset was curated from [`tldr`](https://github.com/tldr-pages/tldr). 
The project aims to provide frequent usage of bash commands with natural language intents. 
For more details, please check the repo.

### Citation Information

```
@article{zhou2022doccoder,
  title={DocCoder: Generating Code by Retrieving and Reading Docs},
  author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham},
  journal={arXiv preprint arXiv:2207.05987},
  year={2022}
}
``` 
task_categories:text2text-generation language_creators:crowdsourced language_creators:expert-generated multilinguality:monolingual size_categories:unknown source_datasets:original language:code license:mit code-generation doc retrieval retrieval augmented generation arxiv:2207.05987 region:us
{"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "DocPrompting-CoNaLa", "tags": ["code-generation", "doc retrieval", "retrieval augmented generation"]}
TAGS
#task_categories-text2text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-code #license-mit #code-generation #doc retrieval #retrieval augmented generation #arxiv-2207.05987 #region-us 


## Dataset Description
- Repository: URL
- Paper: DocPrompting: Generating Code by Retrieving the Docs

### Dataset Summary
This is the natural language to bash generation dataset we harvested from the English subset of 'tldr'
We split the dataset by bash commands. Every command in the dev and test set is held out from the training set.

### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations.

### Languages
English - Bash

## Dataset Structure


### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: the unique id of a question
- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split
- cmd_name: the bash command of this code snippet
- tldr_cmd_name: the bash command used in tldr github repo. The 'cmd_name' and 'tldr_cmd_name' can be different due to naming difference
- manual_exist: whether the manual exists in URL
- matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token.
 

docs:
- doc_id: the id of a doc
- doc_content: the content of the doc

## Dataset Creation
The dataset was curated from 'tldr'. 
The project aims to provide frequent usage of bash commands with natural language intents. 
For more details, please check the repo.



 
## Dataset Description
- Repository: URL
- Paper: DocPrompting: Generating Code by Retrieving the Docs ### Dataset Summary
This is the natural language to bash generation dataset we harvested from the English subset of 'tldr'
We split the dataset by bash commands. Every command in the dev and test set is held out from the training set. ### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations. ### Languages
English - Bash ## Dataset Structure ### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: the unique id of a question
- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split
- cmd_name: the bash command of this code snippet
- tldr_cmd_name: the bash command used in tldr github repo. The 'cmd_name' and 'tldr_cmd_name' can be different due to naming difference
- manual_exist: whether the manual exists in URL
- matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token.
 

docs:
- doc_id: the id of a doc
- doc_content: the content of the doc ## Dataset Creation
The dataset was curated from 'tldr'. 
The project aims to provide frequent usage of bash commands with natural language intents. 
For more details, please check the repo.
TAGS
#task_categories-text2text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-code #license-mit #code-generation #doc retrieval #retrieval augmented generation #arxiv-2207.05987 #region-us 
 ## Dataset Description
- Repository: URL
- Paper: DocPrompting: Generating Code by Retrieving the Docs ### Dataset Summary
This is the natural language to bash generation dataset we harvested from the English subset of 'tldr'
We split the dataset by bash commands. Every command in the dev and test set is held out from the training set. ### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations. ### Languages
English - Bash ## Dataset Structure ### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: the unique id of a question
- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split
- cmd_name: the bash command of this code snippet
- tldr_cmd_name: the bash command used in tldr github repo. The 'cmd_name' and 'tldr_cmd_name' can be different due to naming difference
- manual_exist: whether the manual exists in URL
- matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token.
 

docs:
- doc_id: the id of a doc
- doc_content: the content of the doc ## Dataset Creation
The dataset was curated from 'tldr'. 
The project aims to provide frequent usage of bash commands with natural language intents. 
For more details, please check the repo.