Remove Auto-classification Remove Auto-complete Remove Explainability Remove Natural Language Processing
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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. Let me explain. Our model gets a prompt and auto-completes it.

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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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A Gentle Introduction to GPTs

Mlearning.ai

You don’t need to have a PhD to understand the billion parameter language model GPT is a general-purpose natural language processing model that revolutionized the landscape of AI. GPT-3 is a autoregressive language model created by OpenAI, released in 2020 . What is GPT-3?

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Falcon 2 11B is now available on Amazon SageMaker JumpStart

AWS Machine Learning Blog

It’s a next generation model in the Falcon family—a more efficient and accessible large language model (LLM) that is trained on a 5.5 It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. After deployment is complete, you will see that an endpoint is created.

Python 100
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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.

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Simplify Deployment and Monitoring of Foundation Models with DataRobot MLOps

DataRobot Blog

These developments have allowed researchers to create models that can perform a wide range of natural language processing tasks, such as machine translation, summarization, question answering and even dialogue generation. Then you can use the model to perform tasks such as text generation, classification, and translation.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking.