# Dataset Card for "code_x_glue_cc_clone_detection_poj_104"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits-sample-size)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104

### Dataset Summary

CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104

Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task.

### Supported Tasks and Leaderboards

- `document-retrieval`: The dataset can be used to train a model for retrieving top-k codes with the same semantics.

### Languages

- C++ **programming** language

## Dataset Structure

### Data Instances

An example of 'train' looks as follows.
```
{
    "code": "\nint f(int shu,int min)\n{ \n  int k=1;\n  if(shu < min)\n  { \n    k= 0; \n   return k;\n  } \n  else\n {\n  for(int i = min;i<shu;i++)\n  { \n    if(shu%i == 0)\n    { \n         k=k+ f(shu/i,i); \n    } \n  \n    \n  } \n    return k; \n}\n} \n\nmain()\n{\n      int n,i,a;\n      scanf(\"%d\",&n);\n      \n      for(i=0;i<n;i++)\n      {\n          scanf(\"%d\",&a);\n          \n          if(i!=n-1)                                                        \n           printf(\"%d\\n\",f(a,2));\n           else\n           printf(\"%d\",f(a,2));                           \n                                      \n                     \n                      \n      }              \n                     \n                      \n                      }", 
    "id": 0, 
    "label": "home"
}
```

### Data Fields

In the following each data field in go is explained for each config. The data fields are the same among all splits.

#### default

|field name| type |                 description                  |
|----------|------|----------------------------------------------|
|id        |int32 | Index of the sample                          |
|code      |string| The full text of the function                |
|label     |string| The id of problem that the source code solves|

### Data Splits

| name  |train|validation|test |
|-------|----:|---------:|----:|
|default|32000|      8000|12000|

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

https://github.com/microsoft, https://github.com/madlag

### Licensing Information

Computational Use of Data Agreement (C-UDA) License.

### Citation Information

```
@inproceedings{mou2016convolutional,
  title={Convolutional neural networks over tree structures for programming language processing},
  author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
  booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
  pages={1287--1293},
  year={2016}
}
```

### Contributions

Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
task_categories:text-retrieval task_ids:document-retrieval annotations_creators:found language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:code license:c-uda region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["code"], "license": ["c-uda"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "CodeXGlueCcCloneDetectionPoj104", "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20179075, "num_examples": 32500}, {"name": "validation", "num_bytes": 6382433, "num_examples": 8500}, {"name": "test", "num_bytes": 7227506, "num_examples": 12000}], "download_size": 13348734, "dataset_size": 33789014}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
TAGS
#task_categories-text-retrieval #task_ids-document-retrieval #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-code #license-c-uda #region-us 

Dataset Card for "code\_x\_glue\_cc\_clone\_detection\_poj\_104"
================================================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


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


* Homepage: URL


### Dataset Summary


CodeXGLUE Clone-detection-POJ-104 dataset, available at URL


Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task.


### Supported Tasks and Leaderboards


* 'document-retrieval': The dataset can be used to train a model for retrieving top-k codes with the same semantics.


### Languages


* C++ programming language


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


### Data Instances


An example of 'train' looks as follows.


### Data Fields


In the following each data field in go is explained for each config. The data fields are the same among all splits.


#### default


field name: id, type: int32, description: Index of the sample
field name: code, type: string, description: The full text of the function
field name: label, type: string, description: The id of problem that the source code solves


### Data Splits



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


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


URL URL


### Licensing Information


Computational Use of Data Agreement (C-UDA) License.


### Contributions


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.



### Dataset Summary


CodeXGLUE Clone-detection-POJ-104 dataset, available at URL


Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task. ### Supported Tasks and Leaderboards


* 'document-retrieval': The dataset can be used to train a model for retrieving top-k codes with the same semantics. ### Languages


* C++ programming language


Dataset Structure
----------------- ### Data Instances


An example of 'train' looks as follows. ### Data Fields


In the following each data field in go is explained for each config. The data fields are the same among all splits. #### default


field name: id, type: int32, description: Index of the sample
field name: code, type: string, description: The full text of the function
field name: label, type: string, description: The id of problem that the source code solves ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


URL URL ### Licensing Information


Computational Use of Data Agreement (C-UDA) License. ### Contributions


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
TAGS
#task_categories-text-retrieval #task_ids-document-retrieval #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-code #license-c-uda #region-us 
 ### Dataset Summary


CodeXGLUE Clone-detection-POJ-104 dataset, available at URL


Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task. ### Supported Tasks and Leaderboards


* 'document-retrieval': The dataset can be used to train a model for retrieving top-k codes with the same semantics. ### Languages


* C++ programming language


Dataset Structure
----------------- ### Data Instances


An example of 'train' looks as follows. ### Data Fields


In the following each data field in go is explained for each config. The data fields are the same among all splits. #### default


field name: id, type: int32, description: Index of the sample
field name: code, type: string, description: The full text of the function
field name: label, type: string, description: The id of problem that the source code solves ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


URL URL ### Licensing Information


Computational Use of Data Agreement (C-UDA) License. ### Contributions


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
passage: TAGS
#task_categories-text-retrieval #task_ids-document-retrieval #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-code #license-c-uda #region-us 
### Dataset Summary


CodeXGLUE Clone-detection-POJ-104 dataset, available at URL


Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task.### Supported Tasks and Leaderboards


* 'document-retrieval': The dataset can be used to train a model for retrieving top-k codes with the same semantics.### Languages


* C++ programming language


Dataset Structure
-----------------### Data Instances


An example of 'train' looks as follows.### Data Fields


In the following each data field in go is explained for each config. The data fields are the same among all splits.#### default


field name: id, type: int32, description: Index of the sample
field name: code, type: string, description: The full text of the function
field name: label, type: string, description: The id of problem that the source code solves### Data Splits



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations


Additional Information
----------------------### Dataset Curators


URL URL### Licensing Information


Computational Use of Data Agreement (C-UDA) License.### Contributions


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.