Duplicate from google/code_x_glue_cc_clone_detection_big_clone_bench

Co-authored-by: Parquet-converter (BOT) <parquet-converter@users.noreply.huggingface.co>

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- code
+license:
+- c-uda
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- semantic-similarity-classification
+pretty_name: CodeXGlueCcCloneDetectionBigCloneBench
+dataset_info:
+  features:
+  - name: id
+    dtype: int32
+  - name: id1
+    dtype: int32
+  - name: id2
+    dtype: int32
+  - name: func1
+    dtype: string
+  - name: func2
+    dtype: string
+  - name: label
+    dtype: bool
+  splits:
+  - name: train
+    num_bytes: 2888035029
+    num_examples: 901028
+  - name: validation
+    num_bytes: 1371399358
+    num_examples: 415416
+  - name: test
+    num_bytes: 1220662565
+    num_examples: 415416
+  download_size: 1279275281
+  dataset_size: 5480096952
+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_big_clone_bench"
+
+## 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-BigCloneBench
+
+### Dataset Summary
+
+CodeXGLUE Clone-detection-BigCloneBench dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench
+
+Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
+The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.
+
+### Supported Tasks and Leaderboards
+
+- `semantic-similarity-classification`: The dataset can be used to train a model for classifying if two given java methods are cloens of each other.
+
+### Languages
+
+- Java **programming** language
+
+## Dataset Structure
+
+### Data Instances
+
+An example of 'test' looks as follows.
+```
+{
+    "func1": "    @Test(expected = GadgetException.class)\n    public void malformedGadgetSpecIsCachedAndThrows() throws Exception {\n        HttpRequest request = createCacheableRequest();\n        expect(pipeline.execute(request)).andReturn(new HttpResponse(\"malformed junk\")).once();\n        replay(pipeline);\n        try {\n            specFactory.getGadgetSpec(createContext(SPEC_URL, false));\n            fail(\"No exception thrown on bad parse\");\n        } catch (GadgetException e) {\n        }\n        specFactory.getGadgetSpec(createContext(SPEC_URL, false));\n    }\n", 
+    "func2": "    public InputStream getInputStream() throws TGBrowserException {\n        try {\n            if (!this.isFolder()) {\n                URL url = new URL(this.url);\n                InputStream stream = url.openStream();\n                return stream;\n            }\n        } catch (Throwable throwable) {\n            throw new TGBrowserException(throwable);\n        }\n        return null;\n    }\n", 
+    "id": 0, 
+    "id1": 2381663, 
+    "id2": 4458076, 
+    "label": false
+}
+```
+
+### 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                               |
+|id1       |int32 | The first function id                             |
+|id2       |int32 | The second function id                            |
+|func1     |string| The full text of the first function               |
+|func2     |string| The full text of the second function              |
+|label     |bool  | 1 is the functions are not equivalent, 0 otherwise|
+
+### Data Splits
+
+| name  |train |validation| test |
+|-------|-----:|---------:|-----:|
+|default|901028|    415416|415416|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was mined from the IJaDataset 2.0 dataset.
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+Data was manually labeled by three judges by automatically identifying potential clones using search heuristics.
+[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
+
+Most of the clones are type 1 and 2 with type 3 and especially type 4 being rare.
+
+[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{svajlenko2014towards,
+  title={Towards a big data curated benchmark of inter-project code clones},
+  author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
+  booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
+  pages={476--480},
+  year={2014},
+  organization={IEEE}
+}
+
+@inproceedings{wang2020detecting,
+  title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
+  author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
+  booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
+  pages={261--271},
+  year={2020},
+  organization={IEEE}
+}
+```
+
+### Contributions
+
+Thanks to @madlag (and partly also @ncoop57) for adding this dataset.

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