The Toxic Detection endpoint returns a JSON object containing the original text, a censored version of the text, and a boolean indicating whether the text contains profanity.

The key fields in the response data are "original" (the input text), "censored" (the text with profanity replaced by asterisks), and "has_profanity" (a boolean indicating the presence of offensive language).

The response data is structured as a JSON object with three fields: "original," "censored," and "has_profanity," allowing for easy parsing and utilization in applications.

The primary parameter for the Toxic Detection endpoint is the text input, which users must provide to analyze for toxic language.

Users can customize their requests by varying the input text to analyze different phrases or comments for toxicity, tailoring the moderation to specific content types.

Typical use cases include moderating comments on social media, filtering user-generated content in forums, and ensuring appropriate language in gaming chats.

Data accuracy is maintained through advanced machine learning algorithms that continuously learn from diverse datasets, ensuring effective detection of toxic language across various contexts.

Quality checks include regular updates to the machine learning models, validation against real-world data, and user feedback mechanisms to improve detection capabilities over time.

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