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Code Compilation Task

Download data using huggingface load_dataset()

>>> import datasets
>>> code_compilation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_compilation")
>>> print(code_compilation_dataset)

DatasetDict({
    train: Dataset({
        features: ['lang_cluster', 'source_code', 'file_name', 'compilation_error', 'difficulty', 'src_uid', 'code_uid', 'lang'],
        num_rows: 19915150
    })
    validation: Dataset({
        features: ['lang_cluster', 'source_code', 'file_name', 'compilation_error', 'difficulty', 'src_uid', 'code_uid', 'lang'],
        num_rows: 6394
    })
    test: Dataset({
        features: ['lang_cluster', 'source_code', 'file_name', 'compilation_error', 'difficulty', 'src_uid', 'code_uid', 'lang'],
        num_rows: 30388
    })
})

To download the code_compilation data,

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval
cd xCodeEval
git lfs pull --include "code_compilation/*"
git lfs pull --include "problem_descriptions.jsonl"

Note that code_compilation data is extremely large. Feel free to download a subset of data first.

A Sample from train/validation/test split

{
    "lang": "GNU C++17",
    "source_code": "#include<bits/stdc++.h>\nusing namespace std;\nchar s[200005],t[200005];\nint s1[200005],t1[200005];\nint n;\nint main()\n{\n    cin>>n;\n    cin>>s+1>>t+1;\n    for(int i=1; i<=n; i++)\n        s1[n+1-i]=s[i]-'a'+1,t1[n+1-i]=t[i]-'a'+1;\n\n    for(int i=1; i<=n; i++)\n        s1[i]+=t1[i];\n\n    for(int i=1; i<=n; i++)\n    {\n        if(s1[i]>=26)\n        {\n            s1[i]%=26;\n            s1[i+1]+=1;\n        }\n    }\n\n    if(s1[n+1]) s1[n]+=26;\n\n\n    for(int i=1; i<=n; i++)\n    {\n        if(!s1[i])\n        {\n            s1[i+1]--;\n            s1[i]=26;\n        }\n        if(s1[i]&1)\n            s1[i-1]+=26;\n\n    }\n    for(int i=1; i<=n; i++)\n    {\n        s1[i]/=2;\n        if(s1[i]==0)\n            s1[i]=26,s1[i+1]--;\n    }\n\n    for(int i=n; i>=1; i--)\n        printf(\"%c\",'a'-1+s1[i]);\n\n    return 0;\n}\n",
    "lang_cluster": "C++",
    "compilation_error": false,
    "code_uid": "c6afb1328299497e14ac949dbe60d038",
    "src_uid": "5f4009d4065f5ad39e662095f8f5c068",
    "difficulty": 1900,
}

Key Definitions

  1. lang: Runtime/Compiler version of the source_code.
  2. source_code: A program.
  3. lang_cluster: A generic programming language name the value of lang belongs to.
  4. compilation_error: True/False, Indicates if the code generates a compilation error or not.
  5. code_uid: A unique ID for the sample. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the code_uid.
  6. src_uid: A specific identifier that shows which problem the code is associated with. This identifier is important for the training of the model. The problem referred to by the src_uid provides a natural description of the problem that the code successfully solved. Refer to Structure of problem_descriptions.jsonl
  7. difficulty: Difficulty rating of the problem indicated by src_uid. The higher the harder.

MD5 hash of the data

Run the following,

cd xCodeEval/
tar c code_compilation | md5sum

Output should match, 24854d10e95089b79fca207053b5f1ae.

Tree

3 directories, 421 files

.
├── test
│   ├── C#.jsonl
│   ├── C++.jsonl
│   ├── C.jsonl
│   ├── Go.jsonl
│   ├── Java.jsonl
│   ├── Javascript.jsonl
│   ├── Kotlin.jsonl
│   ├── PHP.jsonl
│   ├── Python.jsonl
│   ├── Ruby.jsonl
│   └── Rust.jsonl
├── train
│   ├── train_0000.jsonl
│   ├── train_0001.jsonl
│   ├── train_0002.jsonl
│   ├── train_0003.jsonl
│   ├── train_0004.jsonl
│   ├── train_0005.jsonl
│   ├── train_0006.jsonl
│   ├── train_0007.jsonl
│   ├── train_0008.jsonl
│   ├── train_0009.jsonl
│   ├── train_0010.jsonl
│   ├── train_0011.jsonl
│   ├── train_0012.jsonl
│   ├── train_0013.jsonl
│   ├── train_0014.jsonl
│   ├── train_0015.jsonl
│   ├── train_0016.jsonl
│   ├── train_0017.jsonl
│   ├── train_0018.jsonl
│   ├── train_0019.jsonl
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│   ├── train_0022.jsonl
│   ├── train_0023.jsonl
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│   ├── train_0026.jsonl
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│   ├── train_0123.jsonl
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│   ├── train_0197.jsonl
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│   ├── train_0199.jsonl
│   ├── train_0200.jsonl
│   ├── train_0201.jsonl
│   ├── train_0202.jsonl
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│   ├── train_0205.jsonl
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│   ├── train_0257.jsonl
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│   ├── train_0265.jsonl
│   ├── train_0266.jsonl
│   ├── train_0267.jsonl
│   ├── train_0268.jsonl
│   ├── train_0269.jsonl
│   ├── train_0270.jsonl
│   ├── train_0271.jsonl
│   ├── train_0272.jsonl
│   ├── train_0273.jsonl
│   ├── train_0274.jsonl
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│   ├── train_0276.jsonl
│   ├── train_0277.jsonl
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│   ├── train_0279.jsonl
│   ├── train_0280.jsonl
│   ├── train_0281.jsonl
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│   ├── train_0340.jsonl
│   ├── train_0341.jsonl
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│   ├── train_0343.jsonl
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│   ├── train_0345.jsonl
│   ├── train_0346.jsonl
│   ├── train_0347.jsonl
│   ├── train_0348.jsonl
│   ├── train_0349.jsonl
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│   ├── train_0351.jsonl
│   ├── train_0352.jsonl
│   ├── train_0353.jsonl
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│   ├── train_0355.jsonl
│   ├── train_0356.jsonl
│   ├── train_0357.jsonl
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│   ├── train_0359.jsonl
│   ├── train_0360.jsonl
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│   ├── train_0364.jsonl
│   ├── train_0365.jsonl
│   ├── train_0366.jsonl
│   ├── train_0367.jsonl
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│   ├── train_0376.jsonl
│   ├── train_0377.jsonl
│   ├── train_0378.jsonl
│   ├── train_0379.jsonl
│   ├── train_0380.jsonl
│   ├── train_0381.jsonl
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│   ├── train_0383.jsonl
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│   ├── train_0387.jsonl
│   ├── train_0388.jsonl
│   ├── train_0389.jsonl
│   ├── train_0390.jsonl
│   ├── train_0391.jsonl
│   ├── train_0392.jsonl
│   ├── train_0393.jsonl
│   ├── train_0394.jsonl
│   ├── train_0395.jsonl
│   ├── train_0396.jsonl
│   ├── train_0397.jsonl
│   └── train_0398.jsonl
└── validation
    ├── C#.jsonl
    ├── C++.jsonl
    ├── C.jsonl
    ├── Go.jsonl
    ├── Java.jsonl
    ├── Javascript.jsonl
    ├── Kotlin.jsonl
    ├── PHP.jsonl
    ├── Python.jsonl
    ├── Ruby.jsonl
    └── Rust.jsonl