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Announcing our $50M Series C to build superhuman Speech AI models

AssemblyAI

This new model is being trained on >10M hours of voice data (1 petabyte) leveraging Google’s new TPU chips — and represents a 1,250x increase in training data compared to the first-ever model made available by AssemblyAI back in 2019.

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Text to Exam Generator (NLP) Using Machine Learning

Mlearning.ai

This is the link [8] to the article about this Zero-Shot Classification NLP. BART stands for Bidirectional and Auto-Regression, and is used in processing human languages that is related to sentences and text. The approach was proposed by Yin et al. The technology that is used in this program is called BART.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. The DJL was created at Amazon and open-sourced in 2019. It is also a fully Apache-2 licensed open-source project and can be found on GitHub.

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Modern NLP: A Detailed Overview. Part 3: BERT

Towards AI

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding BERT was introduced in 2019, by Jacob Devlin and his colleagues from Google. In the NLP world, there are usually two types of models or tasks broadly, auto-regressive models and auto-encoding models.

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A Gentle Introduction to GPTs

Mlearning.ai

Along with text generation it can also be used to text classification and text summarization. The auto-complete feature on your smartphone is based on this principle. When you type “how”, the auto-complete will suggest words like “to” or “are”. It’s architecture can handle a array of natural language processing tasks.

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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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Modern NLP: A Detailed Overview. Part 2: GPTs

Towards AI

Generating Wikipedia By Summarizing Long Sequences This work was published by Peter J Liu at Google in 2019. The architecture is an auto-regressive architecture, i.e., the model produces one word at a time and then takes in the sequence attached with the predicted word, to predict the next word.

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