References:
mlqa-translate-train.ar
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'train' |
78058 |
'validation' |
9512 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'train' |
80069 |
'validation' |
9927 |
- Features:
{
"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.vi
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'train' |
84816 |
'validation' |
10356 |
- Features:
{
"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.zh
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'train' |
76285 |
'validation' |
9568 |
- Features:
{
"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.es
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'train' |
81810 |
'validation' |
10123 |
- Features:
{
"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.hi
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-train.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'train' |
82451 |
'validation' |
10253 |
- Features:
{
"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-test.ar
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5335 |
- Features:
{
"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-test.de
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
4517 |
- Features:
{
"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-test.vi
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5495 |
- Features:
{
"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-test.zh
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5137 |
- Features:
{
"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-test.es
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5253 |
- Features:
{
"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-test.hi
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa-translate-test.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
4918 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5335 |
'validation' |
517 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1649 |
'validation' |
207 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
2047 |
'validation' |
163 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1912 |
'validation' |
188 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.en')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5335 |
'validation' |
517 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1978 |
'validation' |
161 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.ar.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1831 |
'validation' |
186 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1649 |
'validation' |
207 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
4517 |
'validation' |
512 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1675 |
'validation' |
182 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1621 |
'validation' |
190 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.en')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
4517 |
'validation' |
512 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1776 |
'validation' |
196 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.de.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1430 |
'validation' |
163 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
2047 |
'validation' |
163 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1675 |
'validation' |
182 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5495 |
'validation' |
511 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1943 |
'validation' |
184 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.en')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5495 |
'validation' |
511 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
2018 |
'validation' |
189 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.vi.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1947 |
'validation' |
177 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1912 |
'validation' |
188 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1621 |
'validation' |
190 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1943 |
'validation' |
184 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5137 |
'validation' |
504 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.en')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5137 |
'validation' |
504 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1947 |
'validation' |
161 |
- Features:
{
"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.hi
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.zh.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1767 |
'validation' |
189 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5335 |
'validation' |
517 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
4517 |
'validation' |
512 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5495 |
'validation' |
511 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5137 |
'validation' |
504 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.en')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
11590 |
'validation' |
1148 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5253 |
'validation' |
500 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.en.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
4918 |
'validation' |
507 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1978 |
'validation' |
161 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1776 |
'validation' |
196 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
2018 |
'validation' |
189 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1947 |
'validation' |
161 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.en')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5253 |
'validation' |
500 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
5253 |
'validation' |
500 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.es.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1723 |
'validation' |
187 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.ar')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1831 |
'validation' |
186 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.de')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1430 |
'validation' |
163 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.vi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1947 |
'validation' |
177 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.zh')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1767 |
'validation' |
189 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.en')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
4918 |
'validation' |
507 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.es')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
1723 |
'validation' |
187 |
- Features:
{
"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
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:mlqa/mlqa.hi.hi')
- Description:
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.
- License: No known license
- Version: 1.0.0
- Splits:
Split | Examples |
---|---|
'test' |
4918 |
'validation' |
507 |
- Features:
{
"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"
}
}