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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 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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Empowering Model Sharing, Enhanced Annotation, and Azure Blob Backups in NLP Lab

John Snow Labs

We are thrilled to release NLP Lab 5.4 which brings a host of exciting enhancements to further empower your NLP journey. Publish Models Directly into Models HUB We’re excited to introduce a streamlined way to publish NLP models to the NLP Models HUB directly from NLP Lab.

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

AWS Machine Learning Blog

Background of multimodality models Machine learning (ML) models have achieved significant advancements in fields like natural language processing (NLP) and computer vision, where models can exhibit human-like performance in analyzing and generating content from a single source of data. is the script that handles any requests for serving.

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Text to Exam Generator (NLP) Using Machine Learning

Mlearning.ai

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. See the attachment below. The approach was proposed by Yin et al.

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Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

AWS Machine Learning Blog

It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis, and many more. Deep learning (DL) models with more layers and parameters perform better in complex tasks like computer vision and NLP.

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Introducing spaCy v3.0

Explosion

It’s much easier to configure and train your pipeline, and there are lots of new and improved integrations with the rest of the NLP ecosystem. And since modern NLP workflows often consist of multiple steps, there’s a new workflow system to help you keep your work organized. See NLP-progress for more results. Flair 2 89.7

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