- Información general
- Crear modelos
- Consumir modelos
- Automatizaciones en Document Understanding™
- Actividades
- Llamadas a API
- Características generativas
- Paquetes ML
- 1040: paquete ML
- 1040 Anexo C - Paquete ML
- 1040 Anexo D - Paquete ML
- 1040 Anexo E - Paquete ML
- 1040x: paquete ML
- 3949a: paquete ML
- 4506T: paquete ML
- 709: paquete ML
- 9465: paquete ML
- ACORD125: paquete ML
- ACORD126 - Paquete ML
- ACORD131 - Paquete ML
- ACORD140 - Paquete ML
- ACORD25 - Paquete ML
- Extractos bancarios: paquete ML
- Conocimientos de embarque: paquete ML
- Certificado de incorporación: paquete ML
- Certificado de origen: paquete ML
- Cheques: paquete ML
- Certificado de producto secundario: paquete ML
- CMS1500 - Paquete ML
- Declaración de conformidad de la UE: Paquete ML
- Estados financieros: paquete ML
- FM1003: paquete ML
- I9 - Paquete ML
- Documentos de identidad: paquete ML
- Facturas: paquete ML
- FacturasAustralia: paquete ML
- FacturasChina - Paquete ML
- Facturas en hebreo: paquete ML
- FacturasIndia - Paquete ML
- FacturasJapón - Paquete ML
- Envío de facturas: paquete ML
- Listas de embalaje: paquete ML
- Nóminas - - Paquete ML
- Pasaportes: paquete ML
- Órdenes de compra: paquete ML
- Recibos: paquete ML
- ConsejosDeRemesas: paquete ML
- UB04 - Paquete ML
- Facturas de servicios públicos: paquete ML
- Títulos de vehículos: paquete ML
- W2 - Paquete ML
- W9 - Paquete ML
- Puntos finales públicos
- Idiomas admitidos
- Datos y seguridad
- Lógica de licencias y tarificación
- Tutorial
Características generativas
Esta característica forma parte actualmente de un proceso de auditoría y no debe considerarse parte de la autorización de FedRAMP hasta que finalice la revisión. Consulta aquí la lista completa de características actualmente en revisión.
La IA generativa es una forma de tecnología de IA que aprovecha los modelos de aprendizaje automático (ML) para crear y generar nuevo contenido, datos o información.
La clave para la mayoría de las tareas de IA generativas son los grandes modelos de idioma (LLM). Estos son modelos ML que se entrenan en una gran cantidad de datos de texto, diseñados para generar texto similar al humano. Los LLM también pueden comprender y responder a las solicitudes completando oraciones o párrafos de forma similar a la humana.
Primarily applied during the automatic annotation process of documents in the Build step, these generative models accelerate taxonomy design and help in training models efficiently.
Pre-annotation in Document Understanding is done using a combination of generative and specialized models, based on the document type's schema. The schema clearly defines the fields you want to extract from a particular document type.
To get a deeper understanding of how Generative Annotation works and how you can use it efficiently in your projects, check the Annotate documents page.
Generative extraction is a crucial feature within Document UnderstandingTM that uses the power of generative AI models. These models are configured using activities and are primarily used at runtime for data extraction.
Generative extraction is capable of deciphering and extracting specific information from unstructured or semi-structured documents. For instance, it can scan through an invoice and accurately retrieve details such as the date, billed amount, and company name. This enables fast, efficient, and highly accurate information gathering from various types of documents.
- Document Understanding activities package:
- Extract Document Data, Prompt parameter after choosing the Generative extractor.
- Document Understanding ML activities package:
- IntelligentOCR activities package:
- Data Extraction Scope, ApplyAutoValidation parameter.
You can also use Document Understanding APIs to leverage generative extraction features.
Generative classification uses AI models to automatically classify documents immediately after they are uploaded.
This automatic classification process leverages ML models to 'read' the content of a document, understand its context, and consequently classify it into predefined categories. This way, the system can handle and organize multiple types of documents efficiently.
By accurately classifying unstructured or semi-structured documents, Generative Classification improves the document processing workflow, saves time, and enhances the overall document management.
- Document Understanding activities package:
- Document Understanding ML activities package:
You can also use Document Understanding APIs to leverage generative classification features.
Generative validation is a distinctive feature in Document Understanding that plays an important role during the validation process. This feature is primarily used after the extraction step to validate the confidence score for the extraction made using specialized models.
When a ML model's confidence score for a document extraction is low, generative validation is used to cross-check the output. This validation process involves both the specialized and generative ML models working together to ensure accuracy.
If both models yield the same output, human validation can be bypassed, leading to a significant enhancement in the time efficiency of validation. This process not only saves valuable time in the document validation step but also improves the performance of your models by employing a secondary generative model to cross-verify the output, ensuring a higher level of accuracy.
- Document Understanding activities package:
- Extract Document Data, Auto-validation parameter
- IntelligentOCR activities package:
- Data Extraction Scope, ApplyAutoValidation and AutoValidationConfidenceThreshold parameters
You can also use Document Understanding APIs to leverage generative validation features.
