TFDS はCroissant 🥐 形式をサポートするようになりました。詳細については、ドキュメントをお読みください。

このページは Cloud Translation API によって翻訳されました。

mlqa

参考文献:

mlqa-translate-train.ar

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'train'`	78058
`'validation'`	9512

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.de')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'train'`	80069
`'validation'`	9927

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'train'`	84816
`'validation'`	10356

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'train'`	76285
`'validation'`	9568

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'train'`	81810
`'validation'`	10123

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-train.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'train'`	82451
`'validation'`	10253

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5335

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.de')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	4517

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5495

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5137

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5253

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa-translate-test.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	4918

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.ar.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5335
`'validation'`	517

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.ar.de')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1649年
`'validation'`	207

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.ar.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	2047年
`'validation'`	163

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.ar.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1912年
`'validation'`	188

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.ar.en')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5335
`'validation'`	517

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.ar.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1978年
`'validation'`	161

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.ar.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1831年
`'validation'`	186

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.de.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1649年
`'validation'`	207

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.de.de')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	4517
`'validation'`	512

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.de.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1675年
`'validation'`	182

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.de.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1621年
`'validation'`	190

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.de.en')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	4517
`'validation'`	512

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.de.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1776年
`'validation'`	196

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.de.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1430
`'validation'`	163

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.vi.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	2047年
`'validation'`	163

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.vi.de')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1675年
`'validation'`	182

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.vi.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5495
`'validation'`	511

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.vi.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1943年
`'validation'`	184

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.vi.en')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5495
`'validation'`	511

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.vi.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	2018年
`'validation'`	189

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.vi.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1947年
`'validation'`	177

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.zh.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1912年
`'validation'`	188

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.zh.de')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1621年
`'validation'`	190

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.zh.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1943年
`'validation'`	184

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.zh.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5137
`'validation'`	504

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.zh.en')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5137
`'validation'`	504

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.zh.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1947年
`'validation'`	161

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.zh.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1767年
`'validation'`	189

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.en.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5335
`'validation'`	517

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.en.de')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	4517
`'validation'`	512

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.en.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5495
`'validation'`	511

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.en.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5137
`'validation'`	504

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.en.en')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	11590
`'validation'`	1148

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.en.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5253
`'validation'`	500

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.en.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	4918
`'validation'`	507

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.es.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1978年
`'validation'`	161

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.es.de')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1776年
`'validation'`	196

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.es.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	2018年
`'validation'`	189

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.es.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1947年
`'validation'`	161

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.es.en')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5253
`'validation'`	500

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.es.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	5253
`'validation'`	500

特徴：

{
    "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.こんにちは

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.es.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1723年
`'validation'`	187

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.hi.ar')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1831年
`'validation'`	186

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.hi.de')

説明：

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.

ライセンス: 不明なライセンス
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1430
`'validation'`	163

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.hi.vi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1947年
`'validation'`	177

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.hi.zh')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1767年
`'validation'`	189

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.hi.en')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	4918
`'validation'`	507

特徴：

{
    "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

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.hi.es')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	1723年
`'validation'`	187

特徴：

{
    "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.こんにちは。

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:mlqa/mlqa.hi.hi')

説明：

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.

ライセンス: 既知のライセンスはありません
バージョン: 1.0.0
分割:

スプリット	例
`'test'`	4918
`'validation'`	507

特徴：

{
    "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"
    }
}