ecthr_cases

Références:

prédiction-de-violation-alléguée

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:ecthr_cases/alleged-violation-prediction')
  • Descriptif :
The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases.
  • Licence : CC BY-NC-SA (Creative Commons / Attribution-NonCommercial-ShareAlike)
  • Version : 1.1.0
  • Fractionnements :
Diviser Exemples
'test' 1000
'train' 9000
'validation' 1000
  • Caractéristiques :
{
    "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"
    }
}

prédiction de violation

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:ecthr_cases/violation-prediction')
  • Descriptif :
The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases.
  • Licence : CC BY-NC-SA (Creative Commons / Attribution-NonCommercial-ShareAlike)
  • Version : 1.1.0
  • Fractionnements :
Diviser Exemples
'test' 1000
'train' 9000
'validation' 1000
  • Caractéristiques :
{
    "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"
    }
}