multi_eurlex

อ้างอิง:

ห้องน้ำในตัว

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/en')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 55000
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ดา

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/da')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 55000
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

เดอ

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/de')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 55000
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ไม่มี

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/nl')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 55000
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

สว

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/sv')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 42490
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

บีจี

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/bg')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 15986
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ซีเอส

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/cs')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 23187
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ชม

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/hr')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 7944
'validation' 2500
  • คุณสมบัติ :
{
    "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"
    }
}

กรุณา

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/pl')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 23197
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

สค

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/sk')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 22971
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

สล

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/sl')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 23184
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

เช่น

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/es')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 52785
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/fr')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 55000
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

มัน

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/it')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 55000
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

จุด

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/pt')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 52370
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

โร

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/ro')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 15921
'validation' 5,000
  • คุณสมบัติ :
{
    "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

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/et')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 23126
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ฟิ

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/fi')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 42497
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ฮะ

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/hu')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 22664
'validation' 5,000
  • คุณสมบัติ :
{
    "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

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/lt')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 23188
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

เลเวล

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/lv')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 23208
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

เอล

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/el')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 55000
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ภูเขา

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/mt')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 17521
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}

ทุก_ภาษา

ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:

ds = tfds.load('huggingface:multi_eurlex/all_languages')
  • คำอธิบาย :
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).
  • ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
  • เวอร์ชัน : 1.0.0
  • แยก :
แยก ตัวอย่าง
'test' 5,000
'train' 55000
'validation' 5,000
  • คุณสมบัติ :
{
    "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"
    }
}