Referanslar:
tr
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/en')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 55000 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
da
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/da')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 55000 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/de')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 55000 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
nl
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/nl')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 55000 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
sv
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/sv')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 42490 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/bg')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 15986 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/cs')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 23187 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
saat
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/hr')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 7944 |
'validation' | 2500 |
- Özellikler :
{
"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"
}
}
lütfen
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/pl')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 23197 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/sk')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 22971 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/sl')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 23184 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
es
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/es')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 52785 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
Fr
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/fr')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 55000 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
BT
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/it')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 55000 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
puan
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/pt')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 52370 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/ro')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 15921 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
ve
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/et')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 23126 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/fi')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 42497 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
ha
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/hu')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 22664 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/lt')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 23188 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
seviye
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/lv')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 23208 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/el')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 55000 |
'validation' | 5000 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/mt')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 17521 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}
Bütün diller
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:multi_eurlex/all_languages')
- Tanım :
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).
- Lisans : Bilinen lisans yok
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 55000 |
'validation' | 5000 |
- Özellikler :
{
"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"
}
}