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selqa

References:

answer_selection_analysis

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:selqa/answer_selection_analysis')
  • Description:
The SelQA dataset provides crowdsourced annotation for two selection-based question answer tasks, 
answer sentence selection and answer triggering.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 1590
'train' 5529
'validation' 785
  • Features:
{
    "section": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "is_paraphrase": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "topic": {
        "num_classes": 10,
        "names": [
            "MUSIC",
            "TV",
            "TRAVEL",
            "ART",
            "SPORT",
            "COUNTRY",
            "MOVIES",
            "HISTORICAL EVENTS",
            "SCIENCE",
            "FOOD"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "answers": {
        "feature": {
            "dtype": "int32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "candidates": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "q_types": {
        "feature": {
            "num_classes": 7,
            "names": [
                "what",
                "why",
                "when",
                "who",
                "where",
                "how",
                ""
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

answer_selection_experiments

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:selqa/answer_selection_experiments')
  • Description:
The SelQA dataset provides crowdsourced annotation for two selection-based question answer tasks, 
answer sentence selection and answer triggering.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 19435
'train' 66438
'validation' 9377
  • Features:
{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "candidate": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "0",
            "1"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

answer_triggering_analysis

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:selqa/answer_triggering_analysis')
  • Description:
The SelQA dataset provides crowdsourced annotation for two selection-based question answer tasks, 
answer sentence selection and answer triggering.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 1590
'train' 5529
'validation' 785
  • Features:
{
    "section": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "is_paraphrase": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "topic": {
        "num_classes": 10,
        "names": [
            "MUSIC",
            "TV",
            "TRAVEL",
            "ART",
            "SPORT",
            "COUNTRY",
            "MOVIES",
            "HISTORICAL EVENTS",
            "SCIENCE",
            "FOOD"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "q_types": {
        "feature": {
            "num_classes": 7,
            "names": [
                "what",
                "why",
                "when",
                "who",
                "where",
                "how",
                ""
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "candidate_list": {
        "feature": {
            "article": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "section": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "candidates": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "answers": {
                "feature": {
                    "dtype": "int32",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

answer_triggering_experiments

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:selqa/answer_triggering_experiments')
  • Description:
The SelQA dataset provides crowdsourced annotation for two selection-based question answer tasks, 
answer sentence selection and answer triggering.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 59845
'train' 205075
'validation' 28798
  • Features:
{
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "candidate": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "0",
            "1"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}