poleval2019_cyberharcèlement

Références:

tâche01

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

ds = tfds.load('huggingface:poleval2019_cyberbullying/task01')
  • Descriptif :
In Task 6-1, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets
    that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and
    related phenomena.

    In Task 6-2, the participants shall distinguish between three classes of tweets: 0 (non-harmful),
    1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech,
    some of them even putting those two phenomena in the same group. The specific conditions on which we based
    our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research
    will be summarized in an introductory paper for the task, however, the main and definitive condition to 1
    distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying),
    or a public person/entity/large group (hate-speech).
  • Licence : Aucune licence connue
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'test' 1000
'train' 10041
  • Caractéristiques :
{
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 2,
        "names": [
            "0",
            "1"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}

tâche02

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

ds = tfds.load('huggingface:poleval2019_cyberbullying/task02')
  • Descriptif :
In Task 6-1, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets
    that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and
    related phenomena.

    In Task 6-2, the participants shall distinguish between three classes of tweets: 0 (non-harmful),
    1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech,
    some of them even putting those two phenomena in the same group. The specific conditions on which we based
    our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research
    will be summarized in an introductory paper for the task, however, the main and definitive condition to 1
    distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying),
    or a public person/entity/large group (hate-speech).
  • Licence : Aucune licence connue
  • Version : 1.0.0
  • Fractionnements :
Diviser Exemples
'test' 1000
'train' 10041
  • Caractéristiques :
{
    "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "label": {
        "num_classes": 3,
        "names": [
            "0",
            "1",
            "2"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    }
}