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fever

参考:

v1.0

使用以下命令在 TFDS 中加载此数据集:

ds = tfds.load('huggingface:fever/v1.0')
  • 说明
With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1] [2]

The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.

FEVER  V1.0
  • 许可:无已知许可
  • 版本:1.0.0
  • 拆分
拆分 样本
'labelled_dev' 37566
'paper_dev' 18999
'paper_test' 18567
'train' 311431
'unlabelled_dev' 19998
'unlabelled_test' 19998
  • 特征
{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "claim": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "evidence_annotation_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "evidence_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "evidence_wiki_url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "evidence_sentence_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    }
}

v2.0

使用以下命令在 TFDS 中加载此数据集:

ds = tfds.load('huggingface:fever/v2.0')
  • 说明
With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1] [2]

The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.

FEVER  V2.0
  • 许可:无已知许可
  • 版本:2.0.0
  • 拆分
拆分 样本
'validation' 2384
  • 特征
{
    "id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "claim": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "evidence_annotation_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "evidence_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    },
    "evidence_wiki_url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "evidence_sentence_id": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
    }
}

wiki_pages

使用以下命令在 TFDS 中加载此数据集:

ds = tfds.load('huggingface:fever/wiki_pages')
  • 说明
With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. [1] [2]

The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.

Wikipedia pages
  • 许可:无已知许可
  • 版本:1.0.0
  • 拆分
拆分 样本
'wikipedia_pages' 5416537
  • 特征
{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lines": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}