Mlearning.ai

JUNE 22, 2023

In this article, we will delve into the three broad categories of transformer models based on their training methodologies: GPT-like (auto-regressive), BERT-like (auto-encoding), and BART/T5-like (sequence-to-sequence). In such cases, we might not always have a complete sequence we are mapping to/from.

Auto-classification 52

Auto-classification Auto-complete BERT Deep Learning 52

deepsense.ai

OCTOBER 8, 2023

Usually agents will have: Some kind of memory (state) Multiple specialized roles: Planner – to “think” and generate a plan (if steps are not predefined) Executor – to “act” by executing the plan using specific tools Feedback provider – to assess the quality of the execution by means of auto-reflection.

Auto-complete 92

Auto-complete LLM AI AI 92

TheSequence

MAY 24, 2023

This is also where I met Lewis Tunstall and as language models with BERT and GPT-2 started taking off we decided to start working on a textbook about transformer models and the Hugging Face ecosystem. data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ).

Auto-complete 59

Auto-complete ML Python Machine Learning 59

Viso.ai

DECEMBER 22, 2023

This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. Full-Auto: SAM independently predicts segmentation masks in the final stage, showcasing its ability to handle complex and ambiguous scenarios with minimal human intervention.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

AssemblyAI

SEPTEMBER 18, 2023

It offers a simple API for applying LLMs to up to 100 hours of audio data, even exposing endpoints for common use tasks It's smart enough to auto-generate subtitles, identify speakers, and transcribe audio in real time. LeMUR is a framework for applying LLMs to spoken language. Need a scalable solution?

Large Language Models 130

Large Language Models BERT LLM Auto-complete 130

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. The project was completed in a month and deployed to production after a week of testing. We used GPU jobs that help us run jobs that use an instance’s GPUs.

BERT 99

BERT Auto-complete Data Scientist Machine Learning 99

Marktechpost

NOVEMBER 25, 2023

This led them to a deep network design resembling a transformer, which is a completely “white box” in the sense that its optimization target, network operators, and learned representation are all fully interpretable mathematically.

Deep Learning 126

Deep Learning Auto-classification Auto-complete BERT 126

AWS Machine Learning Blog

MARCH 2, 2023

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. The code can be found on the GitHub repo. eks-create.sh

BERT 77

BERT Deep Learning Metadata Auto-complete 77

AWS Machine Learning Blog

MAY 8, 2023

With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. SageMaker MMEs can horizontally scale using an auto scaling policy and provision additional GPU compute instances based on specified metrics. Note that the cell takes around 30 minutes to complete. !docker

ML 85

ML BERT Deep Learning Auto-complete 85

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 pip install transformers==4.25.1 pip install transformers==4.25.1 datarobot==3.0.2 the custom.py print("Creating model deployment.") print("Creating model deployment.")

BERT 52

BERT Large Language Models Natural Language Processing Machine Learning 52

Unite.AI

AUGUST 1, 2023

In zero-shot learning, no examples of task completion are provided in the model. Chain-of-thought Prompting Chain-of-thought prompting leverages the inherent auto-regressive properties of large language models (LLMs), which excel at predicting the next word in a given sequence.

Prompt Engineer 246

Prompt Engineer Prompt Engineering ChatGPT Convolutional Neural Networks 246

Explosion

JULY 6, 2021

For example, you’ll be able to use the information that certain spans of text are definitely not PERSON entities, without having to provide the complete gold-standard annotations for the given example. spacy-dbpedia-spotlight Use DBpedia Spotlight to link entities ✍️ contextualSpellCheck Contextual spell correction using BERT ?

BERT 52

BERT Auto-complete NLP Python 52

Google Research AI blog

FEBRUARY 28, 2023

Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M MultiBERTs Predictions on Winogender Predictions of BERT on Winogender before and after several different interventions. UGIF A multi-lingual, multi-modal UI grounded dataset for step-by-step task completion on the smartphone.

Robotics 72

Robotics Deep Learning Auto-classification ML 72

Snorkel AI

MARCH 2, 2023

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. Others, toward language completion and further downstream tasks. So there’s obviously an evolution. Really quickly, LLMs can do many things.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. Others, toward language completion and further downstream tasks. So there’s obviously an evolution. Really quickly, LLMs can do many things.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Explosion

JANUARY 31, 2021

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 When you load a config, spaCy checks if the settings are complete and if all values have the correct types. Reproducibility with no hidden defaults.

NLP 52

NLP Python BERT Auto-classification 52

Jay Alammar

DECEMBER 16, 2020

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. In generating the second token to complete the date, the name still is the most important with 60% importance, followed by the first portion of the date -- a model output, but an input to the second time step.

Explainability 52

Explainability Auto-complete Auto-classification Neural Network 52

AWS Machine Learning Blog

APRIL 17, 2023

SageMaker LMI containers includes model download optimization by using the s5cmd library to speed up the model download time and container startup times, and eventually speed up auto scaling on SageMaker. A complete example that illustrates the no-code option can be found in the following notebook.

Prompt Engineer 81

Prompt Engineer Prompt Engineering Deep Learning Large Language Models 81

AWS Machine Learning Blog

MARCH 6, 2023

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. This results in faster restarts and workload completion. Cluster update is currently enabled for P and G GPU-based instance types.

Large Language Models 81

Large Language Models LLM Machine Learning ML 81

AWS Machine Learning Blog

NOVEMBER 1, 2023

Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring. The pre-training of IDEFICS-9b took 350 hours to complete on 128 Nvidia A100 GPUs, whereas fine-tuning of IDEFICS-9b-instruct took 70 hours on 128 Nvidia A100 GPUs, both on AWS p4.24xlarge instances.

Auto-classification 96

Auto-classification LLM Auto-complete Generative AI 96

AWS Machine Learning Blog

FEBRUARY 24, 2023

In addition, load testing can help guide the auto scaling strategies using the right metrics rather than iterative trial and error methods. We tested two NLP models: bert-base-uncased (109M) and roberta-large (335M). NLP bert-base-uncased 109M PyTorch 26 62% 70 -39% 105 142% 140 29% TensorRT 42. Diff (%) CV CNN Resnet50 ml.g4dn.2xlarge

BERT 89

BERT NLP Computer Vision Neural Network 89

AWS Machine Learning Blog

JANUARY 9, 2023

The models can be completely heterogenous, with their own independent serving stack. After these business logic steps are complete, the inputs are passed through to ML models. And during idle time, it should be able to turn off compute capacity completely so that you’re not charged. ML inference options.

ML 75

ML Auto-complete Auto-classification Deep Learning 75

Topbots

JUNE 29, 2023

On a more advanced stance, everyone who has done SQL query optimisation will know that many roads lead to the same result, and semantically equivalent queries might have completely different syntax. 3] provides a more complete survey of Text2SQL data augmentation techniques. different variants of semantic parsing.

Auto-complete 52

Auto-complete Algorithm Data Scientist Auto-classification 52