
## 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 re-split of [CoNaLa](https://conala-corpus.github.io/) dataset. 
For each code snippet in the dev and test set, at least one function is held out from the training set. 
This split aims at testing a code generation model's capacity in generating *unseen* functions
We further make sure that examples from the same StackOverflow post (same `question_id` before `-`) are in the same split. 

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

### Languages
English - Python code.

## Dataset Structure
```python
dataset = load_dataset("neulab/docpromting-conala")
DatasetDict({
    train: Dataset({
        features: ['nl', 'cmd', 'question_id', 'cmd_name', 'oracle_man', 'canonical_cmd'],
        num_rows: 2135
    })
    test: Dataset({
        features: ['nl', 'cmd', 'question_id', 'cmd_name', 'oracle_man', 'canonical_cmd'],
        num_rows: 543
    })
    validation: Dataset({
        features: ['nl', 'cmd', 'question_id', 'cmd_name', 'oracle_man', 'canonical_cmd'],
        num_rows: 201
    })
})
})

code_docs = load_dataset("neulab/docprompting-conala", "docs")
DatasetDict({
    train: Dataset({
        features: ['doc_id', 'doc_content'],
        num_rows: 34003
    })
})
```

### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: `x-y`where `x` is the StackOverflow post ID
- oracle_man: The `doc_id` of the functions used in the reference code snippet. The corresponding contents are in `doc` split
- canonical_cmd: The canonical version reference code snippet
 

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

## Dataset Creation
The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original [paper](https://arxiv.org/pdf/1805.08949.pdf)

### 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 arxiv:1805.08949 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 #arxiv-1805.08949 #region-us 


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

### Dataset Summary
This is the re-split of CoNaLa dataset. 
For each code snippet in the dev and test set, at least one function is held out from the training set. 
This split aims at testing a code generation model's capacity in generating *unseen* functions
We further make sure that examples from the same StackOverflow post (same 'question_id' before '-') are in the same split. 

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

### Languages
English - Python code.

## Dataset Structure


### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: 'x-y'where 'x' is the StackOverflow post ID
- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split
- canonical_cmd: The canonical version reference code snippet
 

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

## Dataset Creation
The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper



 
## Dataset Description
- Repository: URL
- Paper: DocPrompting: Generating Code by Retrieving the Docs ### Dataset Summary
This is the re-split of CoNaLa dataset. 
For each code snippet in the dev and test set, at least one function is held out from the training set. 
This split aims at testing a code generation model's capacity in generating *unseen* functions
We further make sure that examples from the same StackOverflow post (same 'question_id' before '-') are in the same split. ### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations. ### Languages
English - Python code. ## Dataset Structure ### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: 'x-y'where 'x' is the StackOverflow post ID
- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split
- canonical_cmd: The canonical version reference code snippet
 

docs:
- doc_id: the id of a doc
- doc_content: the content of the doc ## Dataset Creation
The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper
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 #arxiv-1805.08949 #region-us 
 ## Dataset Description
- Repository: URL
- Paper: DocPrompting: Generating Code by Retrieving the Docs ### Dataset Summary
This is the re-split of CoNaLa dataset. 
For each code snippet in the dev and test set, at least one function is held out from the training set. 
This split aims at testing a code generation model's capacity in generating *unseen* functions
We further make sure that examples from the same StackOverflow post (same 'question_id' before '-') are in the same split. ### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations. ### Languages
English - Python code. ## Dataset Structure ### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: 'x-y'where 'x' is the StackOverflow post ID
- oracle_man: The 'doc_id' of the functions used in the reference code snippet. The corresponding contents are in 'doc' split
- canonical_cmd: The canonical version reference code snippet
 

docs:
- doc_id: the id of a doc
- doc_content: the content of the doc ## Dataset Creation
The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper