code_x_glue_cc_code_completion_token

مراجع:

• شفرة
• معانقة

java

استخدم الأمر التالي لتحميل مجموعة البيانات هذه في TFDS:

ds = tfds.load('huggingface:code_x_glue_cc_code_completion_token/java')

• وصف :

CodeXGLUE CodeCompletion-token dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token

Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.

• الترخيص : لا يوجد ترخيص معروف
• الإصدار : 0.0.0
• الإنشقاقات :

ينقسم	أمثلة
'test'	8268
'train'	12934
'validation'	7189

• سمات :

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "code": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

بيثون

استخدم الأمر التالي لتحميل مجموعة البيانات هذه في TFDS:

ds = tfds.load('huggingface:code_x_glue_cc_code_completion_token/python')

• وصف :

CodeXGLUE CodeCompletion-token dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token

Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.

• الترخيص : لا يوجد ترخيص معروف
• الإصدار : 0.0.0
• الإنشقاقات :

ينقسم	أمثلة
'test'	50000
'train'	100000

• سمات :

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "path": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "code": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}