qangaroo

Tài liệu tham khảo:

y khoa

Sử dụng lệnh sau để tải tập dữ liệu này trong TFDS:

ds = tfds.load('huggingface:qangaroo/medhop')

Sự miêu tả :

We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.

Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.

Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.

The two QAngaroo datasets provide a training and evaluation resource for such methods.

Giấy phép : Không có giấy phép được biết đến
Phiên bản : 1.0.0
Chia tách :

Tách ra	Ví dụ
`'train'`	1620
`'validation'`	342

Đặc trưng :

{
    "query": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "supports": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "candidates": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

đeo mặt nạ_medhop

Sử dụng lệnh sau để tải tập dữ liệu này trong TFDS:

ds = tfds.load('huggingface:qangaroo/masked_medhop')

Sự miêu tả :

We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.

Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.

Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.

The two QAngaroo datasets provide a training and evaluation resource for such methods.

Giấy phép : Không có giấy phép được biết đến
Phiên bản : 1.0.0
Chia tách :

Tách ra	Ví dụ
`'train'`	1620
`'validation'`	342

Đặc trưng :

{
    "query": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "supports": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "candidates": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

wikihop

Sử dụng lệnh sau để tải tập dữ liệu này trong TFDS:

ds = tfds.load('huggingface:qangaroo/wikihop')

Sự miêu tả :

We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.

Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.

Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.

The two QAngaroo datasets provide a training and evaluation resource for such methods.

Giấy phép : Không có giấy phép được biết đến
Phiên bản : 1.0.0
Chia tách :

Tách ra	Ví dụ
`'train'`	43738
`'validation'`	5129

Đặc trưng :

{
    "query": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "supports": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "candidates": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

đeo mặt nạ_wikihop

Sử dụng lệnh sau để tải tập dữ liệu này trong TFDS:

ds = tfds.load('huggingface:qangaroo/masked_wikihop')

Sự miêu tả :

We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.

Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.

Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.

The two QAngaroo datasets provide a training and evaluation resource for such methods.

Giấy phép : Không có giấy phép được biết đến
Phiên bản : 1.0.0
Chia tách :

Tách ra	Ví dụ
`'train'`	43738
`'validation'`	5129

Đặc trưng :

{
    "query": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "supports": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "candidates": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}