Transformers intuition

Basics

Jan Kirenz

Table of contents

• Intuition
• Hugging Face: Transforming the AI Landscape
• State of the Art Examples

Intuition

Transformers, explained: Understand the model behind GPT, BERT, and T5 by Dale Markowitz

Important Architectures

• Convolutional Neural Network (CNN): Vision

• Recurrent Neural Network (RNN): Text

• Transformers: Text and more

Main Characteristics of Transformers

• Positional Encoding

• Attention

• Self-Attention

Popular Transformer Models

• GPT (Generative Pretrained Transformers): GPT-3, GPT-3.5, GPT-4

• BERT (Bidirectional Encoder Representations from Transformers)

• T5 (Text-to-Text Transfer Transformer)

• RoBERTa (Robustly Optimized BERT Pretraining Approach)

Hugging Face: Transforming the AI Landscape

Overview

• AI research organization
• Develop cutting-edge machine learning models and tools
• Popular open-source Transformers models
• Providing state-of-the-art pre-trained models and tools for a wide range of tasks

Key Features

• Supports popular architectures like BERT, GPT, RoBERTa, and T5
• Easy-to-use API for fine-tuning and deploying models
  • Fine-tuning is the process of taking a pre-trained large language model (e.g. roBERTa) and then tweaking it with additional training data to make it perform a second similar task (e.g. sentiment analysis)
• Available in Python, with support for TensorFlow and PyTorch

NLP Tasks

• Language translation
• Text generation
• Question answering
• Text summarization
• Sentiment analysis
• And more!

Model Hub

The Model Hub is a platform for sharing and discovering pre-trained models, contributed by the AI community.

• Access to thousands of pre-trained models
• Easy integration with the Transformers library
• Collaborative environment for researchers and developers

Spaces

Discover ML apps made by the community: Spaces

Datasets

• Hugging Face also provides Datasets
• Over 29,668 datasets available
• Efficient data loading and processing
• Easy integration with the Transformers library

Pipelines

• Hugging Face Pipelines cover common machine learning tasks

• Pre-built, easy-to-use abstractions (almost no code necessary)

• Simplify workflow

State of the Art Examples

AgentGPT

Assemble, configure, and deploy autonomous AI Agents in your browser.

AgentGPT

https://github.com/reworkd/AgentGPT

Microsoft JARVIS

https://github.com/microsoft/JARVIS