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Modern NLP: A Detailed Overview. Part 3: BERT

Towards AI

In this article, we will talk about another and one of the most impactful works published by Google, BERT (Bi-directional Encoder Representation from Transformers) BERT undoubtedly brought some major improvements in the NLP domain. Then, Finally, we come to BERT.

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UC Berkeley Researchers Propose CRATE: A Novel White-Box Transformer for Efficient Data Compression and Sparsification in Deep Learning

Marktechpost

Such a representation makes many subsequent tasks, including those involving vision, classification, recognition and segmentation, and generation, easier. Therefore, encoders, decoders, and auto-encoders can all be implemented using a roughly identical crate design.

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Amazon EC2 DL2q instance for cost-efficient, high-performance AI inference is now generally available

AWS Machine Learning Blog

Model category Number of models Examples​ NLP​ 157 BERT, BART, FasterTransformer, T5, Z-code MOE Generative AI – NLP 40 LLaMA, CodeGen, GPT, OPT, BLOOM, Jais, Luminous, StarCoder, XGen Generative AI – Image 3 Stable diffusion v1.5 opt/qti-aic/exec/qaic-exec -m=bert-base-cased/generatedModels/bert-base-cased_fix_outofrange_fp16.onnx

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Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

AWS Machine Learning Blog

Financial market participants are faced with an overload of information that influences their decisions, and sentiment analysis stands out as a useful tool to help separate out the relevant and meaningful facts and figures. script will create the VPC, subnets, auto scaling groups, the EKS cluster, its nodes, and any other necessary resources.

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

John Snow Labs

The Large Language Model (LLM) understands the customer’s intent, extracts key information from their query, and delivers accurate and relevant answers. They can adapt to new industry trends, regulatory changes, and evolving customer needs, providing up-to-date and relevant information.

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Paper Summary #5 - XLNet: Generalized Autoregressive Pretraining for Language Understanding

Shreyansh Singh

The paper proposes XLNet, a generalized autoregressive pretraining method that enables learning bidirectional contexts over all permutations of the factorization order and overcomes the limitations of BERT due to the autoregressive formulation of XLNet. So, the training objective in the case of BERT becomes - Here m t is 1 when x t is masked.

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What are the Different Types of Transformers in AI

Mlearning.ai

In this article, we will delve into the three broad categories of transformer models based on their training methodologies: GPT-like (auto-regressive), BERT-like (auto-encoding), and BART/T5-like (sequence-to-sequence). Auto Regression is common in more than just Transformers.