scitldr

Références :

Abstrait

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:scitldr/Abstract')

Description :

A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
SCITLDR contains both author-written and expert-derived TLDRs,
where the latter are collected using a novel annotation protocol
that produces high-quality summaries while minimizing annotation burden.

Licence : Licence Apache 2.0
Version : 0.0.0
Divisions :

Diviser	Exemples
`'test'`	618
`'train'`	1992
`'validation'`	619

Caractéristiques :

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        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
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    },
    "source_labels": {
        "feature": {
            "num_classes": 2,
            "names": [
                "non-oracle",
                "oracle"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
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    "rouge_scores": {
        "feature": {
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        },
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        "id": null,
        "_type": "Sequence"
    },
    "paper_id": {
        "dtype": "string",
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    },
    "target": {
        "feature": {
            "dtype": "string",
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            "_type": "Value"
        },
        "length": -1,
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        "_type": "Sequence"
    }
}

AIC

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:scitldr/AIC')

Description :

A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
SCITLDR contains both author-written and expert-derived TLDRs,
where the latter are collected using a novel annotation protocol
that produces high-quality summaries while minimizing annotation burden.

Licence : Licence Apache 2.0
Version : 0.0.0
Divisions :

Diviser	Exemples
`'test'`	618
`'train'`	1992
`'validation'`	619

Caractéristiques :

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    "source": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "source_labels": {
        "feature": {
            "num_classes": 2,
            "names": [
                0,
                1
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "rouge_scores": {
        "feature": {
            "dtype": "float32",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "paper_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "ic": {
        "dtype": "bool_",
        "id": null,
        "_type": "Value"
    },
    "target": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

Texte intégral

Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :

ds = tfds.load('huggingface:scitldr/FullText')

Description :

A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
SCITLDR contains both author-written and expert-derived TLDRs,
where the latter are collected using a novel annotation protocol
that produces high-quality summaries while minimizing annotation burden.

Licence : Licence Apache 2.0
Version : 0.0.0
Divisions :

Diviser	Exemples
`'test'`	618
`'train'`	1992
`'validation'`	619

Caractéristiques :

{
    "source": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "source_labels": {
        "feature": {
            "num_classes": 2,
            "names": [
                "non-oracle",
                "oracle"
            ],
            "names_file": null,
            "id": null,
            "_type": "ClassLabel"
        },
        "length": -1,
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        "_type": "Sequence"
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    "rouge_scores": {
        "feature": {
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        },
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        "id": null,
        "_type": "Sequence"
    },
    "paper_id": {
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        "id": null,
        "_type": "Value"
    },
    "target": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}