ecthr_cases

مراجع:

ادعایی-نقض-پیش بینی

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:ecthr_cases/alleged-violation-prediction')
  • توضیحات : 
The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases.
  • مجوز : CC BY-NC-SA (Creative Commons / Attribution-NonCommercial-ShareAlike)
  • نسخه : 1.1.0
  • تقسیمات :
تقسیم کنید نمونه ها
'test' 1000
'train' 9000
'validation' 1000
  • ویژگی ها : 
{
    "facts": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "labels": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "silver_rationales": {
        "feature": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "gold_rationales": {
        "feature": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

نقض-پیش بینی

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:ecthr_cases/violation-prediction')
  • توضیحات : 
The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases.
  • مجوز : CC BY-NC-SA (Creative Commons / Attribution-NonCommercial-ShareAlike)
  • نسخه : 1.1.0
  • تقسیمات :
تقسیم کنید نمونه ها
'test' 1000
'train' 9000
'validation' 1000
  • ویژگی ها : 
{
    "facts": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "labels": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "silver_rationales": {
        "feature": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}