narrativaqa

Riferimenti:

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:narrativeqa')
  • Descrizione :
The NarrativeQA dataset for question answering on long documents (movie scripts, books). It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers.
  • Licenza : nessuna licenza conosciuta
  • Versione : 0.0.0
  • Divide :
Diviso Esempi
'test' 10557
'train' 32747
'validation' 3461
  • Caratteristiche :
{
    "document": {
        "id": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "kind": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "url": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "file_size": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "word_count": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "start": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "end": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "summary": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "tokens": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "url": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "title": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "text": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    },
    "question": {
        "text": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "tokens": {
            "feature": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "length": -1,
            "id": null,
            "_type": "Sequence"
        }
    },
    "answers": [
        {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "tokens": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            }
        }
    ]
}
,

Riferimenti:

Utilizzare il comando seguente per caricare questo set di dati in TFDS:

ds = tfds.load('huggingface:narrativeqa')
  • Descrizione :
The NarrativeQA dataset for question answering on long documents (movie scripts, books). It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers.
  • Licenza : nessuna licenza conosciuta
  • Versione : 0.0.0
  • Divide :
Diviso Esempi
'test' 10557
'train' 32747
'validation' 3461
  • Caratteristiche :
{
    "document": {
        "id": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "kind": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "url": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "file_size": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "word_count": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "start": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "end": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "summary": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "tokens": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "url": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "title": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "text": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    },
    "question": {
        "text": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "tokens": {
            "feature": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "length": -1,
            "id": null,
            "_type": "Sequence"
        }
    },
    "answers": [
        {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "tokens": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            }
        }
    ]
}