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Showcase/Toxicity Detection
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Toxicity Detection

Multi-label classification for identifying various types of toxic content including hate speech, threats, and harassment.

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This design is available in our showcase. Copy the configuration below to get started.

Inicio rápido:

# Create your project folder
mkdir toxicity-detection
cd toxicity-detection
# Copy config.yaml from above
potato start config.yaml

Detalles

Tipos de anotación

multiselectradio

Dominio

NLPContent Moderation

Casos de uso

Toxicity DetectionContent ModerationHate Speech Detection

Etiquetas

toxicitycontent moderationhate speechmulti-label

Diseños relacionados

HateXplain - Explainable Hate Speech Detection

Multi-task hate speech annotation with classification (hate/offensive/normal), target community identification, and rationale span highlighting. Based on the HateXplain benchmark (Mathew et al., AAAI 2021) - the first dataset covering classification, target identification, and rationale extraction.

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Toxic Spans Detection

Character-level toxic span annotation based on SemEval-2021 Task 5 (Pavlopoulos et al., 2021). Instead of binary toxicity classification, annotators identify the specific words/phrases that make a comment toxic, enabling more nuanced content moderation.

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Dynamic Hate Speech Detection

Hate speech classification with fine-grained type labels based on the Dynamically Generated Hate Speech Dataset (Vidgen et al., ACL 2021). Classify content as hateful or not, then identify hate type (animosity, derogation, dehumanization, threatening, support for hateful entities) and target group.

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