El endpoint Obtener Probabilidad devuelve un objeto JSON que contiene el análisis del texto de entrada. Incluye campos como "confianza," "idioma," "clase_predicha," y "probabilidades," que indican la probabilidad de que el texto sea generado por IA, escrito por humanos o mixto.

Los campos clave en los datos de respuesta incluyen "confianza" (que indica la certeza de la predicción), "idioma" (el idioma detectado del texto), "clase_predicha" (la clasificación como IA, humano o mixto), y "probabilidades" (valores numéricos que representan la probabilidad de cada clasificación).

Los datos de respuesta están estructurados como un objeto JSON. Contiene un objeto de nivel superior con campos para "confianza," "idioma," "clase_predicha," y un objeto "probabilidades" anidado que detalla la probabilidad de generación por IA y por humanos.

El endpoint Obtener Probabilidad acepta un solo parámetro: el texto a analizar. Los usuarios pueden personalizar sus solicitudes proporcionando diferentes entradas de texto para evaluar varios contenidos para generación de IA o humana.

La precisión de los datos se mantiene mediante un entrenamiento continuo de los modelos de aprendizaje automático subyacentes en conjuntos de datos diversos de texto generado por IA y escrito por humanos. Esto ayuda a mejorar los algoritmos de detección con el tiempo.

Los casos de uso típicos incluyen la moderación de contenido para identificar publicaciones generadas por IA, la detección de fraudes en correos electrónicos, comprobaciones de plagio en presentaciones académicas y la garantía de originalidad en la creación de contenido al verificar la autoría del texto.

Los usuarios pueden utilizar los datos devueltos interpretando el puntaje de "confianza" para evaluar la fiabilidad de la predicción. La "clase_predicha" ayuda a determinar la naturaleza del texto, mientras que las "probabilidades" ofrecen información sobre la probabilidad de diferentes tipos de autoría.

Los usuarios pueden esperar patrones de datos estándar donde el puntaje de "confianza" es más alto para las distinciones claras entre texto de IA y texto humano. Para los casos ambiguos, las "probabilidades" pueden mostrar valores más cercanos, lo que indica características mixtas en el texto.

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