อ้างอิง:
mlqa-แปล-train.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'train' | 78058 |
'validation' | 9512 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-translate-train.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'train' | 80069 |
'validation' | 9927 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-train.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'train' | 84816 |
'validation' | 10356 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-train.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'train' | 76285 |
'validation' | 9568 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-train.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'train' | 81810 |
'validation' | 10123 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-train.hi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'train' | 82451 |
'validation' | 10253 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-test.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5335 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-test.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 4517 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-test.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5495 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-test.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5137 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-test.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5253 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa-แปล-test.hi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 4918 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.ar.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5335 |
'validation' | 517 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.ar.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1649 |
'validation' | 207 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.ar.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2047 |
'validation' | 163 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.ar.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | พ.ศ. 2455 |
'validation' | 188 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.ar.en
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.en')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5335 |
'validation' | 517 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.ar.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1978 |
'validation' | 161 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.ar.hi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1831 |
'validation' | 186 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.de.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1649 |
'validation' | 207 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.de.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 4517 |
'validation' | 512 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.de.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1675 |
'validation' | 182 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.de.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1621 |
'validation' | 190 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.de.en
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.en')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 4517 |
'validation' | 512 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.de.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | พ.ศ. 2319 |
'validation' | 196 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.de.hi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1430 |
'validation' | 163 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.vi.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2047 |
'validation' | 163 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.vi.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1675 |
'validation' | 182 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.vi.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5495 |
'validation' | 511 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.vi.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2486 |
'validation' | 184 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.vi.en
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.en')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5495 |
'validation' | 511 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.vi.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2018 |
'validation' | 189 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.vi.hi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2490 |
'validation' | 177 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.zh.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | พ.ศ. 2455 |
'validation' | 188 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.zh.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1621 |
'validation' | 190 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.zh.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2486 |
'validation' | 184 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.zh.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5137 |
'validation' | 504 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.zh.en
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.en')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5137 |
'validation' | 504 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.zh.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2490 |
'validation' | 161 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.zh.สวัสดี
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | พ.ศ. 2310 |
'validation' | 189 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.en.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5335 |
'validation' | 517 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.en.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 4517 |
'validation' | 512 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.en.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5495 |
'validation' | 511 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.en.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5137 |
'validation' | 504 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.en.en
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.en')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 11590 |
'validation' | 1148 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.en.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5253 |
'validation' | 500 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.en.hi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 4918 |
'validation' | 507 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.es.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1978 |
'validation' | 161 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.es.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | พ.ศ. 2319 |
'validation' | 196 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.es.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2018 |
'validation' | 189 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.es.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2490 |
'validation' | 161 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.es.en
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.en')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5253 |
'validation' | 500 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.es.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 5253 |
'validation' | 500 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.es.hi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1723 |
'validation' | 187 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.hi.ar
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.ar')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1831 |
'validation' | 186 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.hi.de
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.de')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1430 |
'validation' | 163 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.hi.vi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.vi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 2490 |
'validation' | 177 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.hi.zh
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.zh')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | พ.ศ. 2310 |
'validation' | 189 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.hi.en
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.en')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 4918 |
'validation' | 507 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.hi.es
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.es')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 1723 |
'validation' | 187 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
mlqa.hi.hi
ใช้คำสั่งต่อไปนี้เพื่อโหลดชุดข้อมูลนี้ใน TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.hi')
- คำอธิบาย :
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
- ใบอนุญาต : ไม่มีใบอนุญาตที่รู้จัก
- เวอร์ชัน : 1.0.0
- แยก :
| แยก | ตัวอย่าง |
|---|---|
'test' | 4918 |
'validation' | 507 |
- คุณสมบัติ :
{
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}