
## Dataset Description
- **Repository:** https://conala-corpus.github.io/
- **Paper:** [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://arxiv.org/pdf/1805.08949.pdf)

### Dataset Summary
[CoNaLa](https://conala-corpus.github.io/) is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.


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

### Languages
English - Python code.

## Dataset Structure
```python
dataset_curated = load_dataset("neulab/conala")
DatasetDict({
    train: Dataset({
        features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
        num_rows: 2379
    })
    test: Dataset({
        features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
        num_rows: 500
    })
})

dataset_mined = load_dataset("neulab/conala", "mined")
DatasetDict({
    train: Dataset({
        features: ['question_id', 'parent_answer_post_id', 'prob', 'snippet', 'intent', 'id'],
        num_rows: 593891
    })
})
```
### Data Instances

#### CoNaLa - curated
This is the curated dataset by annotators
```
{
    'question_id': 41067960,
    'intent': 'How to convert a list of multiple integers into a single integer?',
    'rewritten_intent': "Concatenate elements of a list 'x' of multiple integers to a single integer",
    'snippet': 'sum(d * 10 ** i for i, d in enumerate(x[::-1]))'
}
```

#### CoNaLa - mined
This is the automatically mined dataset before curation
```
{
    'question_id': 34705205,
     'parent_answer_post_id': 34705233,
     'prob': 0.8690001442846342,
     'snippet': 'sorted(l, key=lambda x: (-int(x[1]), x[0]))',
     'intent': 'Sort a nested list by two elements',
     'id': '34705205_34705233_0'
}
```

### Data Fields
Curated:

|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|rewritten_intent|string|Crowdsourced revised intents that try to better reflect the full meaning of the code|
|snippet|string| Code snippet that implements the intent|

Mined:

|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|parent_answer_post_id|int64|Id of the answer post from which the candidate snippet is extracted|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|snippet|string| Code snippet that implements the intent|
|id|string|Unique id for this intent/snippet pair|
|prob|float64|Probability given by the mining model|

### Data Splits
There are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.

## 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

```
@inproceedings{yin2018learning,
  title={Learning to mine aligned code and natural language pairs from stack overflow},
  author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
  booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
  pages={476--486},
  year={2018},
  organization={IEEE}
}
``` 
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 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": "CoNaLa", "tags": ["code-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 #arxiv-1805.08949 #region-us 

Dataset Description
-------------------


* Repository: URL
* Paper: Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow


### Dataset Summary


CoNaLa is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.


### Supported Tasks and Leaderboards


This dataset is used to evaluate code generations.


### Languages


English - Python code.


Dataset Structure
-----------------


### Data Instances


#### CoNaLa - curated


This is the curated dataset by annotators


#### CoNaLa - mined


This is the automatically mined dataset before curation


### Data Fields


Curated:


Field: question\_id, Type: int64, Description: Id of the Stack Overflow question
Field: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)
Field: rewritten\_intent, Type: string, Description: Crowdsourced revised intents that try to better reflect the full meaning of the code
Field: snippet, Type: string, Description: Code snippet that implements the intent


Mined:


Field: question\_id, Type: int64, Description: Id of the Stack Overflow question
Field: parent\_answer\_post\_id, Type: int64, Description: Id of the answer post from which the candidate snippet is extracted
Field: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)
Field: snippet, Type: string, Description: Code snippet that implements the intent
Field: id, Type: string, Description: Unique id for this intent/snippet pair
Field: prob, Type: float64, Description: Probability given by the mining model


### Data Splits


There are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.


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 Summary


CoNaLa is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples. ### Supported Tasks and Leaderboards


This dataset is used to evaluate code generations. ### Languages


English - Python code.


Dataset Structure
----------------- ### Data Instances #### CoNaLa - curated


This is the curated dataset by annotators #### CoNaLa - mined


This is the automatically mined dataset before curation ### Data Fields


Curated:


Field: question\_id, Type: int64, Description: Id of the Stack Overflow question
Field: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)
Field: rewritten\_intent, Type: string, Description: Crowdsourced revised intents that try to better reflect the full meaning of the code
Field: snippet, Type: string, Description: Code snippet that implements the intent


Mined:


Field: question\_id, Type: int64, Description: Id of the Stack Overflow question
Field: parent\_answer\_post\_id, Type: int64, Description: Id of the answer post from which the candidate snippet is extracted
Field: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)
Field: snippet, Type: string, Description: Code snippet that implements the intent
Field: id, Type: string, Description: Unique id for this intent/snippet pair
Field: prob, Type: float64, Description: Probability given by the mining model ### Data Splits


There are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.


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 #arxiv-1805.08949 #region-us 
 ### Dataset Summary


CoNaLa is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples. ### Supported Tasks and Leaderboards


This dataset is used to evaluate code generations. ### Languages


English - Python code.


Dataset Structure
----------------- ### Data Instances #### CoNaLa - curated


This is the curated dataset by annotators #### CoNaLa - mined


This is the automatically mined dataset before curation ### Data Fields


Curated:


Field: question\_id, Type: int64, Description: Id of the Stack Overflow question
Field: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)
Field: rewritten\_intent, Type: string, Description: Crowdsourced revised intents that try to better reflect the full meaning of the code
Field: snippet, Type: string, Description: Code snippet that implements the intent


Mined:


Field: question\_id, Type: int64, Description: Id of the Stack Overflow question
Field: parent\_answer\_post\_id, Type: int64, Description: Id of the answer post from which the candidate snippet is extracted
Field: intent, Type: string, Description: Natural Language intent (i.e., the title of a Stack Overflow question)
Field: snippet, Type: string, Description: Code snippet that implements the intent
Field: id, Type: string, Description: Unique id for this intent/snippet pair
Field: prob, Type: float64, Description: Probability given by the mining model ### Data Splits


There are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.


Dataset Creation
----------------


The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original paper
passage: 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 #arxiv-1805.08949 #region-us 
### Dataset Summary


CoNaLa is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.### Supported Tasks and Leaderboards


This dataset is used to evaluate code generations.### Languages


English - Python code.


Dataset Structure
-----------------### Data Instances#### CoNaLa - curated


This is the curated dataset by annotators#### CoNaLa - mined


This is the automatically mined dataset before curation