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hyperpartisan_news_detection

References:

byarticle

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:hyperpartisan_news_detection/byarticle')
  • Description:
Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.

There are 2 parts:
- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.
- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'train' 645
  • Features:
{
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hyperpartisan": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "published_at": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

bypublisher

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:hyperpartisan_news_detection/bypublisher')
  • Description:
Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.

There are 2 parts:
- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.
- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.
  • License: No known license
  • Version: 1.0.0
  • Splits:
Split Examples
'train' 600000
'validation' 600000
  • Features:
{
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hyperpartisan": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "published_at": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "bias": {
        "num_classes": 5,
        "names": [
            "right",
            "right-center",
            "least",
            "left-center",
            "left"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}