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What are the Different Types of Transformers in AI

Mlearning.ai

While factors like the number of parameters, activation functions, architectural nuances, context sizes, pretraining data corpus, and languages used in training differentiate these models, one often overlooked aspect that can significantly impact their performance is the training process.

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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.

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning Blog

Each business problem is different, each dataset is different, data volumes vary wildly from client to client, and data quality and often cardinality of a certain column (in the case of structured data) might play a significant role in the complexity of the feature engineering process.

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Introduction to Large Language Models (LLMs): An Overview of BERT, GPT, and Other Popular Models

John Snow Labs

Are you curious about the groundbreaking advancements in Natural Language Processing (NLP)? Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. and GPT-4, marked a significant advancement in the field of large language models.

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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. Today, the computer vision project has gained enormous momentum in mobile applications, automated image annotation tools , and facial recognition and image classification applications.

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Text to Exam Generator (NLP) Using Machine Learning

Mlearning.ai

There will be a lot of tasks to complete. I came up with an idea of a Natural Language Processing (NLP) AI program that can generate exam questions and choices about Named Entity Recognition (who, what, where, when, why). This is the link [8] to the article about this Zero-Shot Classification NLP. Let’s begin!

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

AWS Machine Learning Blog

Customers can create the custom metadata using Amazon Comprehend , a natural-language processing (NLP) service managed by AWS to extract insights about the content of documents, and ingest it into Amazon Kendra along with their data into the index. Custom classification is a two-step process.