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Understanding BERT

Mlearning.ai

Pre-training of Deep Bidirectional Transformers for Language Understanding BERT is a language model that can be fine-tuned for various NLP tasks and at the time of publication achieved several state-of-the-art results. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective. 1 Architecture III.2

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ML and NLP Research Highlights of 2021

Sebastian Ruder

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP).   2021 saw the continuation of the development of ever larger pre-trained models. 6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. Credit for the title image: Liu et al.

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Deploy large language models for a healthtech use case on Amazon SageMaker

AWS Machine Learning Blog

In 2021, the pharmaceutical industry generated $550 billion in US revenue. Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. The other data challenge for healthcare customers are HIPAA compliance requirements.

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Nomic AI Releases the First Fully Open-Source Long Context Text Embedding Model that Surpasses OpenAI Ada-002 Performance on Various Benchmarks

Marktechpost

2021), Izacard et al. Initially, a Masked Language Modeling Pretraining phase utilized resources like BooksCorpus and a Wikipedia dump from 2023, employing the bert-base-uncased tokenizer to create data chunks suited for long-context training. Recent advancements, as highlighted by Lewis et al. 2022), and Ram et al.

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How foundation models and data stores unlock the business potential of generative AI

IBM Journey to AI blog

The term “foundation model” was coined by the Stanford Institute for Human-Centered Artificial Intelligence in 2021. BERT (Bi-directional Encoder Representations from Transformers) is one of the earliest LLM foundation models developed. An open-source model, Google created BERT in 2018.

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ACL 2021 Highlights

Sebastian Ruder

ACL 2021 took place virtually from 1–6 August 2021. These models are essentially all variants of the same Transformer architecture.

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The Black Box Problem in LLMs: Challenges and Emerging Solutions

Unite.AI

Flawed Decision Making The opaqueness in the decision-making process of LLMs like GPT-3 or BERT can lead to undetected biases and errors. This presents an inherent tradeoff between scale, capability, and interpretability. Impact of the LLM Black Box Problem 1.

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