multi_eurlex

Referensi:

en

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/en')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 55000
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

ya

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/da')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 55000
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

de

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/de')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 55000
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

tidak

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/nl')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 55000
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

St

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/sv')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 42490
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

bg

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/bg')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 15986
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

cs

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/cs')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 23187
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

jam

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/hr')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 7944
'validation' 2500
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

hal

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/pl')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 23197
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

sk

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/sk')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 22971
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

sl

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/sl')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 23184
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

yaitu

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/es')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 52785
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

NS

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/fr')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 55000
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

dia

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/it')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 55000
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

pt

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/pt')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 52370
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

ro

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/ro')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 15921
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

et

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/et')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 23126
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

fi

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/fi')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 42497
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

huh

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/hu')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 22664
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

lt

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/lt')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 23188
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

lv

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/lv')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 23208
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

el

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/el')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 55000
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

mt

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/mt')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 17521
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

semua_bahasa

Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:

ds = tfds.load('huggingface:multi_eurlex/all_languages')
  • Keterangan :
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
  • Lisensi : Tidak ada lisensi yang diketahui
  • Versi : 1.0.0
  • Perpecahan :
Membelah Contoh
'test' 5000
'train' 55000
'validation' 5000
  • Fitur :
{
    "celex_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "languages": [
            "en",
            "da",
            "de",
            "nl",
            "sv",
            "bg",
            "cs",
            "hr",
            "pl",
            "sk",
            "sl",
            "es",
            "fr",
            "it",
            "pt",
            "ro",
            "et",
            "fi",
            "hu",
            "lt",
            "lv",
            "el",
            "mt"
        ],
        "id": null,
        "_type": "Translation"
    },
    "labels": {
        "feature": {
            "num_classes": 21,
            "names": [
                "100149",
                "100160",
                "100148",
                "100147",
                "100152",
                "100143",
                "100156",
                "100158",
                "100154",
                "100153",
                "100142",
                "100145",
                "100150",
                "100162",
                "100159",
                "100144",
                "100151",
                "100157",
                "100161",
                "100146",
                "100155"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}