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How to Use Hugging Face Pipelines?

Towards AI

A practical guide on how to perform NLP tasks with Hugging Face Pipelines Image by Canva With the libraries developed recently, it has become easier to perform deep learning analysis. Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more.

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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. This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. In retail , SAM could revolutionize inventory management through automated product recognition and categorization.

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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.

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Deploying Large NLP Models: Infrastructure Cost Optimization

The MLOps Blog

NLP models in commercial applications such as text generation systems have experienced great interest among the user. These models have achieved various groundbreaking results in many NLP tasks like question-answering, summarization, language translation, classification, paraphrasing, et cetera.

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How Pixability uses foundation models to accelerate NLP application development by months

Snorkel AI

To help brands maximize their reach, they need to constantly and accurately categorize billions of YouTube videos. Using Snorkel Flow, Pixability leveraged foundation models to build small, deployable classification models capable of categorizing videos across more than 600 different classes with 90% accuracy in just a few weeks.

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

Becoming Human

A typical application of GNN is node classification. The problems that GNNs are used to solve can be divided into the following categories: Node Classification: The goal of this task is to determine the labeling of samples (represented as nodes) by examining the labels of their immediate neighbors (i.e., their neighbors’ labels).

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Build an image-to-text generative AI application using multimodality models on Amazon SageMaker

AWS Machine Learning Blog

For instance, in ecommerce, image-to-text can automate product categorization based on images, enhancing search efficiency and accuracy. CLIP model CLIP is a multi-modal vision and language model, which can be used for image-text similarity and for zero-shot image classification.