John Snow Labs

JUNE 27, 2023

At their core, LLMs are built upon deep neural networks, enabling them to process vast amounts of text and learn complex patterns. In this section, we will provide an overview of two widely recognized LLMs, BERT and GPT, and introduce other notable models like T5, Pythia, Dolly, Bloom, Falcon, StarCoder, Orca, LLAMA, and Vicuna.

Large Language Models 52

Large Language Models BERT Natural Language Processing NLP 52

Towards AI

JULY 20, 2023

BioBERT and similar BERT-based NER models are trained and fine-tuned using a biomedical corpus (or dataset) such as NCBI Disease, BC5CDR, or Species-800. Data formats for inputting data into NER models typically include Pandas DataFrame or text files in CoNLL format (ie. a text file with one word per line).

Data Extraction 98

Data Extraction NLP Natural Language Processing Automation 98

Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

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). 3) The Neural Tangent Kernel What happened?  

NLP 52

NLP ML Neural Network BERT 52

Towards AI

OCTOBER 8, 2023

The main idea of BERTScore is to use a language model that is good at understanding text, like BERT, and use it to evaluate the similarity between two sentences, a Y in your test set and a Y’ representing the model-generated text. I use a BERT WordPiece tokenizer to generate IDs for each token of each sentence, generating a [40, 30523] array.

BERT 69

BERT Automation Neural Network AI 69

ODSC - Open Data Science

JUNE 9, 2023

The 1970s introduced bell bottoms, case grammars, semantic networks, and conceptual dependency theory. In the 90’s we got grunge, statistical models, recurrent neural networks and long short-term memory models (LSTM). It uses a neural network to learn the vector representations of words from a large corpus of text.

Natural Language Processing 52

Natural Language Processing ChatGPT Generative AI BERT 52

Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. All of this made it easy for researchers and practitioners to use BERT.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. All of this made it easy for researchers and practitioners to use BERT.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis. Richong, Z.,

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Viso.ai

JUNE 12, 2024

GPT models are based on transformer-based deep learning neural network architecture. In July 2020, they introduced the GPT-3 model as the most advanced language model with 175 billion parameters. GPT-2 is not just a language model like BERT, it can also generate text. without supervised pre-training. billion parameters.

Large Language Models 52

Large Language Models ChatGPT Computer Vision Neural Network 52

Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. To achieve this, we first chunk each document into segments of roughly 256 tokens, which is half of the maximum BERT LM input length.

BERT 40

BERT Natural Language Processing NLP Neural Network 40

Dlabs.ai

AUGUST 10, 2020

Despite 80% of surveyed businesses wanting to use chatbots in 2020 , how many do you think will implement them well? The solution is based on a Transformer-type neural network, used in the BERT model as well, that has recently triumphed in the field of machine learning and natural language understanding.

Natural Language Processing 52

Natural Language Processing Chatbots Neural Network Machine Learning 52

Explosion

JANUARY 31, 2021

Named Entity Recognition System OntoNotes CoNLL ‘03 spaCy RoBERTa (2020) 89.7 de_dep_news_trf German bert-base-german-cased 99.0 95.8 - es_dep_news_trf Spanish bert-base-spanish-wwm-cased 98.2 94.4 - zh_core_web_trf Chinese bert-base-chinese 92.5 Stanza (StanfordNLP) 1 88.8 Flair 2 89.7 and CoNLL-2003 corpora.

Python 52

Python NLP BERT Auto-classification 52

Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. Just wait until you hear what happened in 2022. Who should I follow? What happened?

Neural Network 52

Neural Network Data Science AI AI 52

Sebastian Ruder

JANUARY 24, 2022

6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. For each input chunk, nearest neighbor chunks are retrieved using approximate nearest neighbor search based on BERT embedding similarity. Advances in Neural Information Processing Systems, 2020. What happened?   wav2vec 2.0:

NLP 52

NLP ML BERT Computational Linguistics 52

Probably Approximately a Scientific Blog

JANUARY 12, 2021

This long-overdue blog post is based on the Commonsense Tutorial taught by Maarten Sap, Antoine Bosselut, Yejin Choi, Dan Roth, and myself at ACL 2020. With that said, the path to machine commonsense is unlikely to be brute force training larger neural networks with deeper layers. Using the AllenNLP demo. Is it still useful?

Natural Language Processing 52

Natural Language Processing BERT NLP Neural Network 52

ML Review

DECEMBER 16, 2019

Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN). The authors also outline that brute-force BERT decoding without semantic parsing works much worse, so use language models wisely ?‍ Thank you for reading!

Machine Learning 52

Machine Learning Neural Network Explainability NLP 52

Applied Data Science

MARCH 11, 2022

In contrast to classification, a supervised learning paradigm, generation is most often done in an unsupervised manner: for example an autoencoder , in the form of a neural network, can capture the statistical properties of a dataset. Notice the plural: GANs are not one but two neural networks that are playing a game.

Neural Network 52

Neural Network Data Science AI AI 52

Marek Rei

NOVEMBER 4, 2024

They annotate a new test set of news data from 2020 and find that performance of certain models holds up very well and the field luckily hasn’t overfitted to the CoNLL 2003 test set. Analysis shows that the final layers of ELECTRA and BERT capture subject-verb agreement errors best. Imperial, Google Research.

Machine Learning 80

Machine Learning NLP Large Language Models LLM 80

Mlearning.ai

JANUARY 17, 2024

Major milestones in the last few years comprised BERT (Google, 2018), GPT-3 (OpenAI, 2020), Dall-E (OpenAI, 2021), Stable Diffusion (Stability AI, LMU Munich, 2022), ChatGPT (OpenAI, 2022). Complex ML problems can only be solved in neural networks with many layers. Deep learning neural network.

Generative AI 98

Generative AI Prompt Engineer Prompt Engineering Large Language Models 98

AWS Machine Learning Blog

SEPTEMBER 11, 2024

In this example figure, features are extracted from raw historical data, which are then are fed into a neural network (NN). In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. PBAs, such as GPUs, can be used for both these steps.

ML 115

ML Deep Learning Algorithm Large Language Models 115

Jay Alammar

DECEMBER 16, 2020

Neuron Activations The Feed Forward Neural Network (FFNN) sublayer is one of the two major components inside a transformer block (in addition to self-attention). Previous work has examined neuron firings inside deep neural networks in both the NLP and computer vision domains.

Explainability 52

Explainability Auto-classification Auto-complete Neural Network 52

Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.

Generative AI 243

Generative AI ChatGPT Neural Network Convolutional Neural Networks 243

Unite.AI

SEPTEMBER 26, 2024

It all started in 2012 with AlexNet, a deep learning model that showed the true potential of neural networks. The momentum continued in 2017 with the introduction of transformer models like BERT and GPT, which revolutionized natural language processing. This was a game-changer.

AI 147

AI AI Neural Network Data Quality 147