mlqa

مراجع:

mlqa-translate-train.ar

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'train' 78058
'validation' 9512
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'train' 80069
'validation' 9927
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'train' 84816
'validation' 10356
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'train' 76285
'validation' 9568
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'train' 81810
'validation' 10123
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'train' 82451
'validation' 10253
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5335
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 4517
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5495
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5137
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5253
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 4918
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.ar.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5335
'validation' 517
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.ar.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1649
'validation' 207
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.ar.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 2047
'validation' 163
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.ar.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1912
'validation' 188
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.ar.en')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5335
'validation' 517
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.ar.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1978
'validation' 161
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.ar.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1831
'validation' 186
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.de.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1649
'validation' 207
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.de.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 4517
'validation' 512
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.de.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1675
'validation' 182
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.de.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1621
'validation' 190
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.de.en')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 4517
'validation' 512
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.de.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1776
'validation' 196
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.de.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1430
'validation' 163
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.vi.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 2047
'validation' 163
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.vi.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1675
'validation' 182
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.vi.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5495
'validation' 511
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.vi.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1943
'validation' 184
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.vi.en')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5495
'validation' 511
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.vi.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 2018
'validation' 189
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.vi.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1947
'validation' 177
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.zh.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1912
'validation' 188
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.zh.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1621
'validation' 190
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.zh.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1943
'validation' 184
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.zh.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5137
'validation' 504
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.zh.en')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5137
'validation' 504
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.zh.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1947
'validation' 161
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.zh.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1767
'validation' 189
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.en.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5335
'validation' 517
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.en.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 4517
'validation' 512
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.en.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5495
'validation' 511
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.en.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5137
'validation' 504
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.en.en')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 11590
'validation' 1148
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.en.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5253
'validation' 500
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.en.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 4918
'validation' 507
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.es.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1978
'validation' 161
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.es.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1776
'validation' 196
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.es.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 2018
'validation' 189
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.es.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1947
'validation' 161
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.es.en')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5253
'validation' 500
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.es.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 5253
'validation' 500
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.es.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1723
'validation' 187
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.hi.ar')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1831
'validation' 186
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.hi.de')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1430
'validation' 163
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.hi.vi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1947
'validation' 177
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.hi.zh')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1767
'validation' 189
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.hi.en')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 4918
'validation' 507
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.hi.es')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 1723
'validation' 187
  • ویژگی ها : 
{
    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:mlqa/mlqa.hi.hi')
  • توضیحات :
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.
  • مجوز : مجوز شناخته شده ای وجود ندارد
  • نسخه : 1.0.0
  • تقسیم ها :
تقسیم کنید نمونه ها
'test' 4918
'validation' 507
  • ویژگی ها :
{
    "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"
    }
}