John Snow Labs

JUNE 27, 2023

Are you curious about the groundbreaking advancements in Natural Language Processing (NLP)? Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. and GPT-4, marked a significant advancement in the field of large language models.

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John Snow Labs

JUNE 6, 2023

Many different transformer models have already been implemented in Spark NLP, and specifically for text classification, Spark NLP provides various annotators that are designed to work with pretrained language models. BERT (Bidirectional Encoder Representations from Transformers) is a language model that was introduced by Google in 2018.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. BERT can be fine-tuned for a variety of NLP tasks, including question answering, natural language inference, and sentiment analysis.

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Topbots

APRIL 11, 2023

In the rapidly evolving field of artificial intelligence, natural language processing has become a focal point for researchers and developers alike. We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. billion word corpus).

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Towards AI

DECEMBER 28, 2023

Later, Python gained momentum and surpassed all programming languages, including Java, in popularity around 2018–19. The introduction of attention mechanisms has notably altered our approach to working with deep learning algorithms, leading to a revolution in the realms of computer vision and natural language processing (NLP).

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IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. A specific kind of foundation model known as a large language model (LLM) is trained on vast amounts of text data for NLP tasks. An open-source model, Google created BERT in 2018.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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Towards AI

JULY 20, 2023

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

Data Extraction 77

Data Extraction NLP Natural Language Processing Automation 77

Explosion

AUGUST 1, 2019

Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. Transformers and transfer-learning Natural Language Processing (NLP) systems face a problem known as the “knowledge acquisition bottleneck”. We have updated our library and this blog post accordingly.

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Heartbeat

MARCH 22, 2023

RoBERTa: A Modified BERT Model for NLP — by Khushboo Kumari An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019.

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Sebastian Ruder

AUGUST 18, 2019

Throughout its history, most of the major improvements on this task have been driven by different forms of transfer learning: from early self-supervised learning with auxiliary tasks ( Ando and Zhang, 2005 ) and phrase & word clusters ( Lin and Wu, 2009 ) to the language model embeddings ( Peters et al., 2018 ; Akbik et al.,

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NLP BERT Natural Language Processing Computational Linguistics 75

Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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BERT Natural Language Processing Large Language Models Neural Network 83

NLP People

JANUARY 2, 2019

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). BERT is a new milestone in NLP. 7-Have we Finally Solved Machine Translation?

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BERT Natural Language Processing Computational Linguistics NLP 52

Sebastian Ruder

FEBRUARY 24, 2021

Fine-tuning a pre-trained language model (LM) has become the de facto standard for doing transfer learning in natural language processing. Over the last three years ( Ruder, 2018 ), fine-tuning ( Howard & Ruder, 2018 ) has superseded the use of feature extraction of pre-trained embeddings ( Peters et al.,

Natural Language Processing 52

Natural Language Processing BERT NLP Computer Vision 52

Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. In the 2010s, this research intersected with the then-bustling field of neural networks, setting the ground for the first large language model.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. In the 2010s, this research intersected with the then-bustling field of neural networks, setting the ground for the first large language model.

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Large Language Models BERT Neural Network LLM 52

Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). Improving Language Understanding by Generative Pre-Training Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

Sebastian Ruder

OCTOBER 26, 2019

In particular, I cover unsupervised deep multilingual models such as multilingual BERT. Depending on the task, there are other ways of transferring information across languages such as by domain adaptation (as seen above), annotation projection ( Padó & Lapata, 2009 ; Ni et al., 2018 ; Artetxe et al., 2016 ; Eger et al.,

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BERT NLP Neural Network Natural Language Processing 52

Lexalytics

APRIL 5, 2021

This is the sort of representation that is useful for natural language processing. ELMo would also be the first of the Muppet-themed language models that would come to include ERNIE [ 120 ], Grover [ 121 ]….and The base model of BERT [ 103 ] had 12 (!) layers of bidirectional Transformers.

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Viso.ai

JUNE 12, 2024

Viso Suite is the Computer Vision Enterprise Platform Large Language Models Text generation as a tool is already being applied in journalism (news production), education (production and misuse of materials), law (drafting contracts), medicine (diagnostics), science (search and generation of scientific papers), etc.

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AWS Machine Learning Blog

MAY 31, 2023

Large language models In recent years, language models have seen a huge surge in size and popularity. In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks.

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Automation Python BERT Prompt Engineer 84

Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP).  2020 saw the development of ever larger language and dialogue models such as Meena ( Adiwardana et al.,

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Viso.ai

FEBRUARY 25, 2023

Transformer models have become the de-facto status quo in Natural Language Processing (NLP). For example, the popular ChatGPT AI chatbot is a transformer-based language model. They are based on the transformer architecture, which was originally proposed for natural language processing (NLP) in 2017.

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Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Sebastian Ruder

JANUARY 6, 2020

 Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., A whole range of BERT variants have been applied to multimodal settings, mostly involving images and videos together with text (for an example see the figure below). 2019 ) and other variants. VideoBERT ( Sun et al., 2019 ; Wu et al.,

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Sebastian Ruder

NOVEMBER 14, 2022

Models that allow interaction via natural language have become ubiquitious. Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. Transfer learning in natural language processing.

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics BERT 52

Sebastian Ruder

DECEMBER 6, 2021

Reading Comprehension assumes a gold paragraph is provided Standard approaches for reading comprehension build on pre-trained models such as BERT. Using BERT for reading comprehension involves fine-tuning it to predict a) whether a question is answerable and b) whether each token is the start and end of an answer span.

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BERT NLP Natural Language Processing Computational Linguistics 52

Explosion

DECEMBER 28, 2019

The update fixed outstanding bugs on the tracker, gave the docs a huge makeover, improved both speed and accuracy, made installation significantly easier and faster, and added some exciting new features, like ULMFit/BERT/ELMo-style language model pretraining. ✨ Mar 20: A few days later, we upgraded Prodigy to v1.8

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Google Research AI blog

FEBRUARY 28, 2023

For example, supporting equitable student persistence in computing research through our Computer Science Research Mentorship Program , where Googlers have mentored over one thousand students since 2018 — 86% of whom identify as part of a historically marginalized group.

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Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). 6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. Advances in Neural Information Processing Systems, 2020. Credit for the title image: Liu et al. Why is it important?  

NLP 52

NLP ML BERT Computational Linguistics 52

Sebastian Ruder

AUGUST 15, 2021

NLP is all about pre-trained Transformers This should come as no surprise but it's still interesting to see that among the 14 "hot" topics of 2021 (see below) were five pre-trained models (BERT, RoBERTa, BART, GPT-2, XLM-R) and one general "Language models" topic.

NLP 52

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Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them?

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Explosion

MARCH 17, 2019

of the spaCy Natural Language Processing library includes a huge number of features, improvements and bug fixes. spaCy is an open-source library for industrial-strength natural language processing in Python. Clearly, we couldn’t use a model such as BERT or GPT-2 directly. Version 2.1 Devlin et al.

NLP 52

NLP Python Neural Network Natural Language Processing 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The recent history of PBAs begins in 1999, when NVIDIA released its first product expressly marketed as a GPU, designed to accelerate computer graphics and image processing. In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN.

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deepsense.ai

NOVEMBER 14, 2023

Within Natural Language Processing (NLP), ‘pseudo-evaluation’ approaches that we call ‘Superficial Utility Comparison Kriterion’ ( SUCK ) methods, like BLEU [32], METEOR [33], ROUGE [34], or BLEURT [35], attempt to salvage the situation. 2023 [link] [link] [link] BERTScore: Evaluating text generation with BERT , Zhang T.,

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Generative AI Prompt Engineer Prompt Engineering ML 111

Marktechpost

APRIL 16, 2024

In recent years, there has been a great inclination toward Large Language Models (LLMs) due to their amazing text generation, analysis, and classification capabilities. These models use billions of parameters to execute a variety of Natural Language Processing (NLP) tasks.

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AWS Machine Learning Blog

AUGUST 7, 2023

As an added inherent challenge, natural language processing (NLP) classifiers are historically known to be very costly to train and require a large set of vocabulary, known as a corpus , to produce accurate predictions. Later versions of the GPT model, namely GPT3 and GPT4, are the engine that powers the ChatGPT application.

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Towards AI

JULY 19, 2023

John on Patmos | Correggio NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER The NLP Cypher | 02.14.21 mlpen/Nystromformer Transformers have emerged as a powerful workhorse for a broad range of natural language processing tasks. The Vision of St. Heartbreaker Hey Welcome back! torch==1.2.0…

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