MATH_QA

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

Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options.

{
    "Problem": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "Rationale": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "options": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "correct": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "annotated_formula": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "linear_formula": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}