code_x_glue_cc_code_completion_token

อ้างอิง:

ชวา

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:code_x_glue_cc_code_completion_token/java')
  • คำอธิบาย :
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.
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชั่น : 0.0.0
  • แยก :
แยก ตัวอย่าง
'test' 8268
'train' 12934
'validation' 7189
  • คุณสมบัติ :
{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "code": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

หลาม

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:code_x_glue_cc_code_completion_token/python')
  • คำอธิบาย :
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.
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชั่น : 0.0.0
  • แยก :
แยก ตัวอย่าง
'test' 50,000
'train' 100000
  • คุณสมบัติ :
{
    "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"
    }
}