TFDS תומך כעת בפורמט קרואסון 🥐 ! קרא את התיעוד כדי לדעת יותר.

דף זה תורגם על ידי Cloud Translation API.

קודה

הפניות:

קודה

השתמש בפקודה הבאה כדי לטעון מערך נתונים זה ב-TFDS:

ds = tfds.load('huggingface:codah/codah')

תיאור :

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.

רישיון : אין רישיון ידוע
גרסה : 1.0.0
פיצולים :

לְפַצֵל	דוגמאות
`'train'`	2776

מאפיינים :

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question_category": {
        "num_classes": 6,
        "names": [
            "Idioms",
            "Reference",
            "Polysemy",
            "Negation",
            "Quantitative",
            "Others"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "question_propmt": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "candidate_answers": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "correct_answer_idx": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    }
}

fold_0

השתמש בפקודה הבאה כדי לטעון מערך נתונים זה ב-TFDS:

ds = tfds.load('huggingface:codah/fold_0')

תיאור :

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.

רישיון : אין רישיון ידוע
גרסה : 1.0.0
פיצולים :

לְפַצֵל	דוגמאות
`'test'`	555
`'train'`	1665
`'validation'`	556

מאפיינים :

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question_category": {
        "num_classes": 6,
        "names": [
            "Idioms",
            "Reference",
            "Polysemy",
            "Negation",
            "Quantitative",
            "Others"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "question_propmt": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "candidate_answers": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "correct_answer_idx": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    }
}

fold_1

השתמש בפקודה הבאה כדי לטעון מערך נתונים זה ב-TFDS:

ds = tfds.load('huggingface:codah/fold_1')

תיאור :

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.

רישיון : אין רישיון ידוע
גרסה : 1.0.0
פיצולים :

לְפַצֵל	דוגמאות
`'test'`	555
`'train'`	1665
`'validation'`	556

מאפיינים :

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question_category": {
        "num_classes": 6,
        "names": [
            "Idioms",
            "Reference",
            "Polysemy",
            "Negation",
            "Quantitative",
            "Others"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "question_propmt": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "candidate_answers": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "correct_answer_idx": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    }
}

קיפול_2

השתמש בפקודה הבאה כדי לטעון מערך נתונים זה ב-TFDS:

ds = tfds.load('huggingface:codah/fold_2')

תיאור :

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.

רישיון : אין רישיון ידוע
גרסה : 1.0.0
פיצולים :

לְפַצֵל	דוגמאות
`'test'`	555
`'train'`	1665
`'validation'`	556

מאפיינים :

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question_category": {
        "num_classes": 6,
        "names": [
            "Idioms",
            "Reference",
            "Polysemy",
            "Negation",
            "Quantitative",
            "Others"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "question_propmt": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "candidate_answers": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "correct_answer_idx": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    }
}

קיפול_3

השתמש בפקודה הבאה כדי לטעון מערך נתונים זה ב-TFDS:

ds = tfds.load('huggingface:codah/fold_3')

תיאור :

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.

רישיון : אין רישיון ידוע
גרסה : 1.0.0
פיצולים :

לְפַצֵל	דוגמאות
`'test'`	555
`'train'`	1665
`'validation'`	556

מאפיינים :

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question_category": {
        "num_classes": 6,
        "names": [
            "Idioms",
            "Reference",
            "Polysemy",
            "Negation",
            "Quantitative",
            "Others"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "question_propmt": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "candidate_answers": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "correct_answer_idx": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    }
}

fold_4

השתמש בפקודה הבאה כדי לטעון מערך נתונים זה ב-TFDS:

ds = tfds.load('huggingface:codah/fold_4')

תיאור :

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.

רישיון : אין רישיון ידוע
גרסה : 1.0.0
פיצולים :

לְפַצֵל	דוגמאות
`'test'`	556
`'train'`	1665
`'validation'`	555

מאפיינים :

{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "question_category": {
        "num_classes": 6,
        "names": [
            "Idioms",
            "Reference",
            "Polysemy",
            "Negation",
            "Quantitative",
            "Others"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "question_propmt": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "candidate_answers": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "correct_answer_idx": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    }
}