AWS Machine Learning Blog

DECEMBER 4, 2023

In this post, we explain how we built an end-to-end product category prediction pipeline to help commercial teams by using Amazon SageMaker and AWS Batch , reducing model training duration by 90%. 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.

BERT 119

BERT Auto-complete Data Scientist Machine Learning 119

IBM Journey to AI blog

JULY 19, 2023

At re:Invent 2022, IBM Consulting was awarded the Global Innovation Partner of the Year and the GSI Partner of the Year for Latin America , cementing client and AWS trust in IBM Consulting as a partner of choice when it comes to AWS. in 10 years, from 2012 to 2022. Fairness : AI models should treat all groups equitably.

Generative AI 189

Generative AI Large Language Models AI AI 189

Marek Rei

NOVEMBER 4, 2024

EMNLP 2022. link] Proposes an explainability method for language modelling that explains why one word was predicted instead of a specific other word. Adapts three different explainability methods to this contrastive approach and evaluates on a dataset of minimally different sentences. EMNLP 2022. NeurIPS 2022.

Machine Learning 80

Machine Learning NLP Large Language Models LLM 80

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.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings? All we need is the vectors for the words.

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

Convolutional Neural Networks 159

Convolutional Neural Networks Neural Network BERT Deep Learning 159

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. Link-aware LM Pretraining.

BERT 40

BERT Natural Language Processing NLP Neural Network 40

Unite.AI

AUGUST 1, 2023

2022), innovatively adds the phrase “Let's think step by step” to the original prompt. The fundamental working of React can be explained through an instance from HotpotQA, a task requiring high-order reasoning. This approach, introduced by Kojima et al.

Prompt Engineering 246

Prompt Engineering Prompt Engineer ChatGPT Convolutional Neural Networks 246

Flipboard

FEBRUARY 16, 2023

In October 2022, we launched Amazon EC2 Trn1 Instances , powered by AWS Trainium , which is the second generation machine learning accelerator designed by AWS. In this post, we use a Hugging Face BERT-Large model pre-training workload as a simple example to explain how to useTrn1 UltraClusters.

Large Language Models 132

Large Language Models LLM BERT Deep Learning 132

Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. BERT), or consist of both (e.g.,

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. BERT), or consist of both (e.g.,

Large Language Models 52

Large Language Models BERT Neural Network LLM 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). And it will change everything. Let us dive into the wild world of genAI. You can create your own GPT and offer it to other users.

Generative AI 98

Generative AI Prompt Engineering Prompt Engineer Large Language Models 98

Dlabs.ai

MAY 22, 2022

The instructors are very good at explaining complex topics in an easy-to-understand way. We wrote about him in our article on the Top AI Influencers To Follow In 2022. Machine Learning Author: Andrew Ng Everyone interested in machine learning has heard of Andrew Ng : one of the most respected people in the AI world.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Neural Network 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. ExplainaBoard: An Explainable Leaderboard for NLP.

NLP 52

NLP Explainability Neural Network Natural Language Processing 52

Bugra Akyildiz

DECEMBER 18, 2022

7:08 PM ∙ Dec 18, 2022 Articles Dramatron is a system that uses large language models that could be useful for authors for co-writing theatre scripts and screenplays. Dragon can be used as a drop-in replacement for BERT.

Neural Network 52

Neural Network ChatGPT Machine Learning Deep Learning 52

Jay Alammar

OCTOBER 3, 2022

V2 Nov 2022 : Updated images for more precise description of forward diffusion. Let’s start to look under the hood because that helps explain the components, how they interact, and what the image generation options/parameters mean. The released Stable Diffusion model uses ClipText (A GPT-based model ), while the paper used BERT.

Neural Network 98

Neural Network Explainability BERT Computer Vision 98

Dlabs.ai

MARCH 9, 2022

So if a client has suggestions or requests, remember to listen intently and make sure everything is explained and understood by both sides. The book explains how to use the most well-known Python libraries, including Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter, making for a great resource for anyone just starting out.

Data Scientist 52

Data Scientist Machine Learning Python Data Science 52

Heartbeat

NOVEMBER 17, 2023

Considerations for Choosing a Distance Metric for Text Embeddings: Scale or Magnitude : Embeddings from models like Word2Vec, FastText, BERT, and GPT are often normalized to unit length. In the context of this code, it seems to be applied to vectors by determining the proportion of differing vector elements. ", " *bd{2}-d{2}-d{4}b.*"])

Data Scientist 52

Data Scientist Deep Learning Algorithm BERT 52

Bugra Akyildiz

FEBRUARY 5, 2023

This blog post that explains various tricks to make the training faster and allow you to train transformers on a much higher model accuracy. Transformers is a flexible architecture that powers most of the recent modeling innovations, but they are not very easy to train and it takes a lot of compute and memory to be able to train them well.

Large Language Models 52

Large Language Models Neural Network Machine Learning BERT 52

John Snow Labs

MAY 15, 2023

Explainability and Interpretability: Models should be capable of answer stakeholder questions about the decision-making processes of AI systems. 2022 ] showed that adding any mention of ethnicity to a patient note reduces their predicted risk of mortality – with the most accurate model producing the largest error.

NLP 52

NLP Data Scientist Software Engineer Responsible AI 52

ODSC - Open Data Science

FEBRUARY 14, 2023

Since its release on November 30, 2022 by OpenAI , the ChatGPT public demo has taken the world by storm. Some examples of large language models include GPT (Generative Pre-training Transformer), BERT (Bidirectional Encoder Representations from Transformers), and RoBERTa (Robustly Optimized BERT Approach).

ChatGPT 124

ChatGPT Large Language Models OpenAI Conversational AI 124

Heartbeat

JANUARY 9, 2024

LLMs received a lot of media attention when ChatGPT was released in December 2022. BERT and GPT are examples. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

Large Language Models 52

Large Language Models Natural Language Processing LLM Machine Learning 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. For more work on this topic, check out the EvoNLP workshop at EMNLP 2022. Why is it important?  

NLP 52

NLP ML BERT Computational Linguistics 52

Explosion

MAY 17, 2023

In 2014 I started working on spaCy , and here’s an excerpt of how I explained the motivation for the library: Computers don’t understand text. In their experiments, OpenAI prompted GPT3 with 32 examples of each task, and found that they were able to achieve similar accuracy to the BERT baselines. The results in Section 3.7,

LLM 135

LLM NLP Large Language Models OpenAI 135

Topbots

JUNE 29, 2023

4] In the open-source camp, initial attempts at solving the Text2SQL puzzle were focussed on auto-encoding models such as BERT, which excel at NLU tasks.[5, We will focus on the six requirements that seem most important for the task: accuracy, scalability, speed, explainability, privacy and adaptability over time.

Auto-complete 52

Auto-complete Algorithm Data Scientist Auto-classification 52