ML @ CMU

FEBRUARY 24, 2023

Prompting has emerged as a promising approach to solving a wide range of NLP problems using large pre-trained language models (LMs), including left-to-right models such as GPT s and masked LMs such as BERT , RoBERTa , etc. BERT and GPTs) for both classification and generation tasks. Webson and Pavlick (2021) , Zhao et al.,

BERT 101

BERT NLP Large Language Models Natural Language Processing 101

Mlearning.ai

MARCH 2, 2023

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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BERT NLP Deep Learning Neural Network 52

Mlearning.ai

APRIL 8, 2023

Popular Examples include the Bidirectional Encoder Representations from Transformers (BERT) model and the Generative Pre-trained Transformer 3 (GPT-3) model. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al. 2018) “ Language models are few-shot learners ” by Brown et al.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

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. Additionally, data extraction can be more difficult to automate than other SLR elements. a text file with one word per line).

Data Extraction 82

Data Extraction NLP Natural Language Processing Automation 82

Mlearning.ai

OCTOBER 2, 2023

Famous models like BERT and others, begin their journey with initial training on massive datasets encompassing vast swaths of internet text. ArXiv , 2021, /abs/2106.10199. So, fine-tuning is a way that helps improve model performance by training on specific examples of prompts and desired responses. Attention Is All You Need.”

Large Language Models 98

Large Language Models BERT Categorization LLM 98

Sebastian Ruder

JANUARY 24, 2022

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.

NLP 52

NLP ML BERT Computational Linguistics 52

Mlearning.ai

FEBRUARY 15, 2023

transformer.ipynb” uses the BERT architecture to classify the behaviour type for a conversation uttered by therapist and client, i.e, The fourth model which is also used for multi-class classification is built using the famous BERT architecture. The architecture of BERT is represented in Figure 14. 438 therapist_input 0.60

BERT 52

BERT NLP Natural Language Processing Algorithm 52

NVIDIA Developer

JUNE 2, 2021

Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized NLP by offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more.

Natural Language Processing 40

Natural Language Processing Neural Network Deep Learning BERT 40

John Snow Labs

MAY 15, 2023

2021 ] – sometimes changing the likely answer more than 80% of the time. 2021 ] is the mislabeling of training examples. In addition, state-of-the-art question answering models have been shown to exhibit biases around race, gender, physical appearance, disability, and religion [ Parrish et. Finally, [ van Aken et.

NLP 52

NLP Software Engineer Data Scientist Responsible AI 52

Bugra Akyildiz

DECEMBER 18, 2022

Dragon is a new foundation model (improvement of BERT) that is pre-trained jointly from text and knowledge graphs for improved language, knowledge and reasoning capabilities. Dragon can be used as a drop-in replacement for BERT. The following is my hypothesis- 🧵⬇ 7:42 PM ∙ Sep 8, 2021 154 Likes 13 Retweets

Neural Network 52

Neural Network ChatGPT Machine Learning Deep Learning 52

Heartbeat

AUGUST 2, 2023

Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), BERT, XLNet, and Transformer-XL, etc., It has become the backbone of many successful language models, like GPT-3, BERT, and their variants. They are usually trained on a massive amount of text data. Benefits of Using Language Models 1.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering OpenAI 52

Sebastian Ruder

NOVEMBER 14, 2022

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. For a recent study [3] , we similarly reviewed papers from ACL 2021 and found that almost 70% of papers only evaluate on English. 2020) (Ahia et al.,

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics AI 52

Applied Data Science

JANUARY 4, 2022

Hiding your 2021 resolution list under a glass of champagne? To write this post we shook the internet upside down for industry news and research breakthroughs and settled on the following 5 themes, to wrap up 2021 in a neat bow: ? In 2021, the following were added to the ever growing list of Transformer applications.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 52

Jay Alammar

JANUARY 18, 2021

This is likely a similar effect to that observed in BERT of the final layer being the most task-specific. Retrieved from [link] BibTex: @misc{alammar2021hiddenstates, title={Finding the Words to Say: Hidden State Visualizations for Language Models}, author={Alammar, J}, year={2021}, url={[link] }

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BERT Explainability 72

AWS Machine Learning Blog

DECEMBER 4, 2023

An important aspect of our strategy has been the use of SageMaker and AWS Batch to refine pre-trained BERT models for seven different languages. Fine-tuning multilingual BERT models with AWS Batch GPU jobs We sought a solution to support multiple languages for our diverse user base.

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BERT Auto-complete Data Scientist Machine Learning 94

IBM Journey to AI blog

AUGUST 1, 2023

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.

Generative AI 151

Generative AI Data Scientist BERT Machine Learning 151

Applied Data Science

DECEMBER 23, 2022

Then came the Transformer architecture, which solved the issue of long-range dependencies, and, along with it, t he family of BERT models , GPT-2 and its larger successor, GPT-3. This trend started in 2021, with OpenAI Codex , a GPT-3 based tool. The debate was on again: maybe language generation is really just a prediction task?

Neural Network 52

Neural Network AI AI Data Science 52

Snorkel AI

MARCH 1, 2023

BERT BERT, an acronym that stands for “Bidirectional Encoder Representations from Transformers,” was one of the first foundation models and pre-dated the term by several years. 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

AssemblyAI

NOVEMBER 8, 2023

We’ve used the DistilBertTokenizer , which inherits from the BERT WordPiece tokenization scheme. 2021 (Google Research) propose an approach to Truecasing that hierarchically exploits word and character-level encoding. 2021 shared a pretty comprehensive survey on punctuation and casing restoration. billion words).

Neural Network 147

Neural Network BERT Large Language Models Deep Learning 147

Sebastian Ruder

DECEMBER 6, 2021

This post expands on the EMNLP 2021 tutorial on Multi-domain Multilingual Question Answering. Reading Comprehension assumes a gold paragraph is provided Standard approaches for reading comprehension build on pre-trained models such as BERT. 2021 ), among others. The tutorial was organised by Avi Sil and me.

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BERT NLP Natural Language Processing Computational Linguistics 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.

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BERT Natural Language Processing NLP Neural Network 40

DataRobot Blog

FEBRUARY 2, 2023

As an example, getting started with a BERT model for question answering (bert-large-uncased-whole-word-masking-finetuned-squad) is as easy as executing these lines: !pip writefile $BASE_PATH/custom.py """ Copyright 2021 DataRobot, Inc. Let’s create a JSON to ask a question to our question-answering BERT model.

BERT 52

BERT Large Language Models Natural Language Processing Machine Learning 52

The Stanford AI Lab Blog

JANUARY 21, 2022

2021 with natural language descriptions for each video. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. RoBERTa: A Robustly Optimized BERT Pretraining Approach. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. Chen, Suraj Nair, Chelsea Finn. Neumann, M.,

Robotics 40

Robotics Computational Linguistics Computer Vision BERT 40

Sebastian Ruder

FEBRUARY 24, 2021

2021) fine-tune on around 50 labelled datasets in a massively multi-task setting and observe that a large, diverse collection of tasks is important for good transfer performance. For instance, Dou and Neubig (2021) fine-tune a model for word alignment with an objective that teaches it to identify parallel sentences, among others.

Natural Language Processing 52

Natural Language Processing BERT NLP Computer Vision 52

NVIDIA Developer

MAY 20, 2021

Chung , posted May 21 2021 at 12:03AM Businesses worldwide are using artificial intelligence to solve their greatest challenges. Languages: English Overview Title: Fundamentals of Deep Learning Date: Thursday, June 10, 2021, 09:00 AM +05 Status: Open Businesses worldwide are using artificial intelligence to solve their greatest challenges.

Natural Language Processing 40

Natural Language Processing Deep Learning Neural Network NLP 40

Towards AI

AUGUST 10, 2023

And in 2021, they unveiled LaMDA — an LLM rivaling the capabilities of GPT-3. Google also has open-source models like BERT, T5, ViT, and EfficientNet for easy deployment on GCP. Access to Google’s AI models is provided through Model Garden, a managed service on the Google Cloud Platform (GCP).

LLM 94

LLM Large Language Models OpenAI AI 94

Topbots

JULY 6, 2023

Like other large language models, including BERT and GPT-3, LaMDA is trained on terabytes of text data to learn how words relate to one another and then predict what words are likely to come next. How is the problem approached?

LLM 52

LLM Large Language Models Natural Language Processing OpenAI 52

Viso.ai

FEBRUARY 25, 2023

The Vision Transformer (ViT) model architecture was introduced in a research paper published as a conference paper at ICLR 2021 titled “An Image is Worth 16*16 Words: Transformers for Image Recognition at Scale” It was developed and published by Neil Houlsby, Alexey Dosovitskiy, and 10 more authors of the Google Research Brain Team.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

John Snow Labs

JUNE 5, 2023

Let's just peek into the pre-BERT world… For creating models, we need words to be represented in a form n understood by the training network, ie, numbers. This is called a pre-trained model. This pre-trained model is then fine-tuned for each NLP tasks according to need.

NLP 52

NLP Natural Language Processing Deep Learning Algorithm 52

Snorkel AI

MARCH 8, 2023

The agenda today is to first learn how to build a unified foundation model, which is a unit paper from ICCV (International Conference on Computer Vision) 2021. BERT shares this common domain across all of the NLP tasks. As you might know, in the NLP domain, BERT has been the starting foundation model.

NLP 52

NLP BERT Computer Vision Natural Language Processing 52

Snorkel AI

MARCH 8, 2023

The agenda today is to first learn how to build a unified foundation model, which is a unit paper from ICCV (International Conference on Computer Vision) 2021. BERT shares this common domain across all of the NLP tasks. As you might know, in the NLP domain, BERT has been the starting foundation model.

NLP 52

NLP BERT Computer Vision Natural Language Processing 52

Sebastian Ruder

JANUARY 19, 2021

A plethora of language-specific BERT models have been trained for languages beyond English such as AraBERT ( Antoun et al., This is similar to findings for distilling an inductive bias into BERT ( Kuncoro et al., Powerful multilingual models that cover around 100 languages emerged including XML-R ( Conneau et al., 2020 ; Rust et al.,

NLP 52

NLP ML Computer Vision Natural Language Processing 52

DataRobot Blog

MARCH 9, 2022

The company was acquired by DataRobot in 2021. These embeddings are sometimes trained jointly with the model, but usually additional accuracy can be attained by using pre-trained embeddings such as Word2Vec, GloVe, BERT, or FastText. This article was originally published at Algorithimia’s website. and discern what’s behind it.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

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).

Generative AI 98

Generative AI Prompt Engineer Prompt Engineering Large Language Models 98

Snorkel AI

APRIL 18, 2023

For that, the teams actually looked into the 2021 IPCC, NASEM, and USGCRP reports. So for each of these high-level climate change hazards, some of these hazards are already reported in the 2021 IPCC report. These are all different reports that talk about various types of climate change and climate change hazards.

NLP 52

NLP Computer Scientist BERT Categorization 52

Snorkel AI

APRIL 18, 2023

For that, the teams actually looked into the 2021 IPCC, NASEM, and USGCRP reports. So for each of these high-level climate change hazards, some of these hazards are already reported in the 2021 IPCC report. These are all different reports that talk about various types of climate change and climate change hazards.

NLP 52

NLP Computer Scientist BERT Categorization 52

Chatbots Life

MAY 12, 2023

There are many approaches to language modelling, we can for example ask the model to fill in the words in the middle of a sentence (as in the BERT model) or predict which words have been swapped for fake ones (as in the ELECTRA model). The most recent training data is of ChatGPT from 2021 September.

ChatGPT 105

ChatGPT Software Engineer NLP Prompt Engineer 105