code_x_glue_cc_code_refinement

References:

medium

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:code_x_glue_cc_code_refinement/medium')
  • Description:
CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement

We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length.
  • License: No known license
  • Version: 0.0.0
  • Splits:
Split Examples
'test' 6545
'train' 52364
'validation' 6546
  • Features:
{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "buggy": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "fixed": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

small

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:code_x_glue_cc_code_refinement/small')
  • Description:
CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement

We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length.
  • License: No known license
  • Version: 0.0.0
  • Splits:
Split Examples
'test' 5835
'train' 46680
'validation' 5835
  • Features:
{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "buggy": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "fixed": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}