Hugging Face Transformers

Hugging Face Transformers

Hugging Face is a comprehensive platform that provides tools and libraries for natural language processing (NLP) and machine learning, ideal for educators and developers alike. My presentation begins with an overview of Hugging Face, detailing its mission to democratize AI through open-source contributions and accessible resources. We delve into the Transformers Python library, which supports state-of-the-art models like BERT, GPT-3, and T5, enabling sophisticated NLP tasks. I also highlight the importance of prompt engineering, particularly through Streamlit apps, which offer user-friendly interfaces for model training and evaluation. This is followed by an exploration of Transformers JavaScript, demonstrating how to bring powerful NLP capabilities to web applications. Practical applications are emphasized through Exercises 1 and 2, where participants gain hands-on experience with these tools. Finally, I briefly touch on AutoTrain, a feature that streamlines the training process, allowing for quick and efficient model development with minimal manual intervention.

My Bio Slide
Upcoming Meetups
Ice Breaker
AI Arms Race
Our Reasoning Capture!
Keep in Mind When Prompting
Dentons
Year of Devices
Today's Agenda
All Abut Transformers on Hugging Face
Hugging Face Spaces
My Enhanced Workflow
Models
Your First Transform
What is Sreamlit
Sentiment Analysis Application in Streamlit
Choosing Models
Streamlined Development
Types of Pipelines
Transformer JS
Feeding the Error Back into the LLM (Claude or ChatGPT)
Important Code
Comparing Transformers PY to JS
Exercise 1
Exercise 1 Solution (without Streamlit)
Adding Steamlit
With Streamlit
Exercise 2 - Convert to Transformers JS
Just Use a Simple Prompt
It is that easy!
AutoTrain
Questions


































Impressive lineup of topics, Michael—this looks like a valuable resource for anyone keen on mastering Hugging Face Transformers and their practical applications!

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