codah

References:

codah

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:codah/codah')
  • Description:
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.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'train' 2776
  • Features:
{
    "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

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:codah/fold_0')
  • Description:
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.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test' 555
'train' 1665
'validation' 556
  • Features:
{
    "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

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:codah/fold_1')
  • Description:
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.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test' 555
'train' 1665
'validation' 556
  • Features:
{
    "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_2

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:codah/fold_2')
  • Description:
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.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test' 555
'train' 1665
'validation' 556
  • Features:
{
    "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_3

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:codah/fold_3')
  • Description:
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.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test' 555
'train' 1665
'validation' 556
  • Features:
{
    "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

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:codah/fold_4')
  • Description:
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.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'test' 556
'train' 1665
'validation' 555
  • Features:
{
    "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"
    }
}