Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
code
Size:
10K - 100K
License:
| 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-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. |