Natural_questions

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

The NQ corpus contains questions from real users, and it requires QA systems to
read and comprehend an entire Wikipedia article that may or may not contain the
answer to the question. The inclusion of real user questions, and the
requirement that solutions should read an entire page to find the answer, cause
NQ to be a more realistic and challenging task than prior QA datasets.

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "document": {
        "title": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "url": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "html": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "tokens": {
            "feature": {
                "token": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "is_html": {
                    "dtype": "bool",
                    "id": null,
                    "_type": "Value"
                }
            },
            "length": -1,
            "id": null,
            "_type": "Sequence"
        }
    },
    "question": {
        "text": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "tokens": {
            "feature": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "length": -1,
            "id": null,
            "_type": "Sequence"
        }
    },
    "annotations": {
        "feature": {
            "id": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "long_answer": {
                "start_token": {
                    "dtype": "int64",
                    "id": null,
                    "_type": "Value"
                },
                "end_token": {
                    "dtype": "int64",
                    "id": null,
                    "_type": "Value"
                },
                "start_byte": {
                    "dtype": "int64",
                    "id": null,
                    "_type": "Value"
                },
                "end_byte": {
                    "dtype": "int64",
                    "id": null,
                    "_type": "Value"
                }
            },
            "short_answers": {
                "feature": {
                    "start_token": {
                        "dtype": "int64",
                        "id": null,
                        "_type": "Value"
                    },
                    "end_token": {
                        "dtype": "int64",
                        "id": null,
                        "_type": "Value"
                    },
                    "start_byte": {
                        "dtype": "int64",
                        "id": null,
                        "_type": "Value"
                    },
                    "end_byte": {
                        "dtype": "int64",
                        "id": null,
                        "_type": "Value"
                    },
                    "text": {
                        "dtype": "string",
                        "id": null,
                        "_type": "Value"
                    }
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "yes_no_answer": {
                "num_classes": 2,
                "names": [
                    "NO",
                    "YES"
                ],
                "names_file": null,
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}