샤크

참고자료:

샤크

TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.

ds = tfds.load('huggingface:sharc/sharc')
  • 설명 :
ShARC is a Conversational Question Answering dataset focussing on question answering from texts containing rules. The goal is to answer questions by possibly asking follow-up questions first. It is assumed assume that the question is often underspecified, in the sense that the question does not provide enough information to be answered directly. However, an agent can use the supporting rule text to infer what needs to be asked in order to determine the final answer.
  • 라이센스 : 알려진 라이센스 없음
  • 버전 : 1.0.0
  • 분할 :
나뉘다
'train' 21890
'validation' 2270
  • 특징 :
{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "utterance_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "source_url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "snippet": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "scenario": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "history": [
        {
            "follow_up_question": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "follow_up_answer": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        }
    ],
    "evidence": [
        {
            "follow_up_question": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "follow_up_answer": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        }
    ],
    "answer": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "negative_question": {
        "dtype": "bool_",
        "id": null,
        "_type": "Value"
    },
    "negative_scenario": {
        "dtype": "bool_",
        "id": null,
        "_type": "Value"
    }
}