Remove Auto-classification Remove Auto-complete Remove Categorization Remove Natural Language Processing
article thumbnail

Understanding Graph Neural Network with hands-on example| Part-1

Becoming Human

A typical application of GNN is node classification. GNNs are a hybrid of an information diffusion mechanism and neural networks that are used to process data, representing a set of transition functions and a set of output functions. Graph Classification: The goal here is to categorize the entire graph into various categories.

article thumbnail

Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

The custom metadata helps organizations and enterprises categorize information in their preferred way. The insurance provider receives payout claims from the beneficiary’s attorney for different insurance types, such as home, auto, and life insurance. Custom classification is a two-step process.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

How to Use Hugging Face Pipelines?

Towards AI

Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more. The NLP tasks we’ll cover are text classification, named entity recognition, question answering, and text generation. The pipeline we’re going to talk about now is zero-hit classification.

article thumbnail

Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

You can deploy this solution with just a few clicks using Amazon SageMaker JumpStart , a fully managed platform that offers state-of-the-art foundation models for various use cases such as content writing, code generation, question answering, copywriting, summarization, classification, and information retrieval.

LLM 105
article thumbnail

Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

AWS Machine Learning Blog

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific terms or words. This can be achieved by updating the endpoint’s inference units (IUs).

IDP 77
article thumbnail

Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

Key strengths of VLP include the effective utilization of pre-trained VLMs and LLMs, enabling zero-shot or few-shot predictions without necessitating task-specific modifications, and categorizing images from a broad spectrum through casual multi-round dialogues.

article thumbnail

Segment Anything Model (SAM) Deep Dive – Complete 2024 Guide

Viso.ai

Its creators took inspiration from recent developments in natural language processing (NLP) with foundation models. Today, the computer vision project has gained enormous momentum in mobile applications, automated image annotation tools , and facial recognition and image classification applications.