- "NONE" (no toxicity found)
- "HATE" (hateful content)
- "RECLAIMED" (reclaimed language)
Toxicity and hate speech detection models are becoming increasingly important in online platforms where user-generated content can often contain harmful or abusive language. These AI models are designed to automatically identify and flag potentially toxic or hateful content, allowing moderators to review and take action if necessary. However, the effectiveness of these models largely depends on the quality of the training data used to develop them. In recent years, there has been a growing recognition of the need to train these models on "reclaimed" language data, which involves using examples of harmful language that have been re-appropriated and reclaimed by the communities they were originally used to oppress. By training on this type of data, toxicity and hate speech detection models can better understand the nuances of language use and improve their ability to accurately identify and contextualize harmful content.