...경

ds = tfds.load('huggingface:circa')

The Circa (meaning ‘approximately’) dataset aims to help machine learning systems
to solve the problem of interpreting indirect answers to polar questions.

The dataset contains pairs of yes/no questions and indirect answers, together with
annotations for the interpretation of the answer. The data is collected in 10
different social conversational situations (eg. food preferences of a friend).

Note: There might be missing labels in the dataset and we have replaced them with -1.
The original dataset contains no train/dev/test splits.

{
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question-X": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "canquestion-X": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answer-Y": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "judgements": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "goldstandard1": {
        "num_classes": 8,
        "names": [
            "Yes",
            "No",
            "In the middle, neither yes nor no",
            "Probably yes / sometimes yes",
            "Probably no",
            "Yes, subject to some conditions",
            "Other",
            "I am not sure how X will interpret Y\u2019s answer"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "goldstandard2": {
        "num_classes": 5,
        "names": [
            "Yes",
            "No",
            "In the middle, neither yes nor no",
            "Yes, subject to some conditions",
            "Other"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}