عددی_ذوب شده

مراجع:

• کد
• صورت در آغوش گرفته

شناسایی

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

ds = tfds.load('huggingface:numeric_fused_head/identification')

• توضیحات :

Fused Head constructions are noun phrases in which the head noun is missing and is said to be "fused" with its dependent modifier. This missing information is implicit and is important for sentence understanding.The missing heads are easily filled in by humans,  but pose a challenge for computational models.

For example, in the sentence: "I bought 5 apples but got only 4.", 4 is a Fused-Head, and the missing head is apples, which appear earlier in the sentence.

This is a crowd-sourced dataset of 10k numerical fused head examples (1M tokens).

• مجوز : MIT
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "tokens": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "start_index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "end_index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "neg",
            "pos"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

وضوح

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

ds = tfds.load('huggingface:numeric_fused_head/resolution')

• توضیحات :

Fused Head constructions are noun phrases in which the head noun is missing and is said to be "fused" with its dependent modifier. This missing information is implicit and is important for sentence understanding.The missing heads are easily filled in by humans,  but pose a challenge for computational models.

For example, in the sentence: "I bought 5 apples but got only 4.", 4 is a Fused-Head, and the missing head is apples, which appear earlier in the sentence.

This is a crowd-sourced dataset of 10k numerical fused head examples (1M tokens).

• مجوز : MIT
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "tokens": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "line_indices": {
        "feature": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "head": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "speakers": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "anchors_indices": {
        "feature": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

مراجع:

• کد
• صورت در آغوش گرفته

شناسایی

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

ds = tfds.load('huggingface:numeric_fused_head/identification')

• توضیحات :

Fused Head constructions are noun phrases in which the head noun is missing and is said to be "fused" with its dependent modifier. This missing information is implicit and is important for sentence understanding.The missing heads are easily filled in by humans,  but pose a challenge for computational models.

For example, in the sentence: "I bought 5 apples but got only 4.", 4 is a Fused-Head, and the missing head is apples, which appear earlier in the sentence.

This is a crowd-sourced dataset of 10k numerical fused head examples (1M tokens).

• مجوز : MIT
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "tokens": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "start_index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "end_index": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "neg",
            "pos"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

وضوح

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

ds = tfds.load('huggingface:numeric_fused_head/resolution')

• توضیحات :

Fused Head constructions are noun phrases in which the head noun is missing and is said to be "fused" with its dependent modifier. This missing information is implicit and is important for sentence understanding.The missing heads are easily filled in by humans,  but pose a challenge for computational models.

For example, in the sentence: "I bought 5 apples but got only 4.", 4 is a Fused-Head, and the missing head is apples, which appear earlier in the sentence.

This is a crowd-sourced dataset of 10k numerical fused head examples (1M tokens).

• مجوز : MIT
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "tokens": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "line_indices": {
        "feature": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "head": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "speakers": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "anchors_indices": {
        "feature": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}