Datasets:
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-*
Dataset Card for "code_x_glue_cc_clone_detection_poj_104"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
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.