code_x_glue_cc_code_completion_token

Referanslar:

java

Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:

ds = tfds.load('huggingface:code_x_glue_cc_code_completion_token/java')
  • Tanım :
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.
  • Lisans : Bilinen lisans yok
  • Sürüm : 0.0.0
  • Bölünmeler :
Bölmek Örnekler
'test' 8268
'train' 12934
'validation' 7189
  • Özellikler :
{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "code": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

piton

Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:

ds = tfds.load('huggingface:code_x_glue_cc_code_completion_token/python')
  • Tanım :
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.
  • Lisans : Bilinen lisans yok
  • Sürüm : 0.0.0
  • Bölünmeler :
Bölmek Örnekler
'test' 50000
'train' 100000
  • Özellikler :
{
    "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"
    }
}