The Detector endpoint returns a JSON object containing the detected languages and their corresponding confidence scores. This allows users to understand not only the identified languages but also the level of certainty associated with each detection.

The key fields in the response data include "status," "timestamp," and "data." The "data" field contains an array of detected languages paired with their confidence scores, indicating the likelihood that the text is in that language.

The returned data is in JSON format. It includes a "status" field indicating the request's success, a "timestamp" for when the request was processed, and a "data" array with language-confidence pairs, e.g., [["english", 0.32], ["french", 0.21]].

The Detector endpoint accepts a single parameter: the text input for language detection. Users can submit a few sentences, and the API will analyze this text to determine the language.

The response data is organized into three main components: a status message, a timestamp, and a data array. The data array contains sub-arrays for each detected language and its confidence score, allowing for easy parsing and analysis.

Typical use cases include content filtering, customer support routing, translation services, multilingual chatbots, and sentiment analysis. The API helps automate language detection, enhancing user experience in multilingual environments.

Data accuracy is maintained through advanced algorithms and machine learning models that continuously learn from diverse text samples. Regular updates and quality checks ensure the API remains reliable in detecting various languages.

If the API returns partial or empty results, users should check the input text for clarity and completeness. Implementing error handling in the application can help manage such scenarios, ensuring a fallback mechanism is in place for user experience.

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