Hugging Face Transformers for NLP and LLM Applications

Uplatz offers this comprehensive course on Hugging Face Transformers for NLP and LLM Applications. It is a self-paced course with video lectures. You will be awarded Course Completion Certificate at the end of the course.

Hugging Face Transformers is like a toolbox filled with amazing tools for understanding and working with human language. It's a popular open-source library that gives you access to powerful pre-trained models, making it much easier to build AI applications that can read, write, and understand text.

How they work

Imagine a super smart computer program that has read millions of books, articles, and websites. This program has learned the patterns and rules of language, and it can use this knowledge to understand new text and even generate its own. That's essentially what these "Transformer" models are. They are a special kind of neural network designed to be really good at processing language.

The library provides these pre-trained models, so you don't have to start from scratch. You can use them directly or teach them new tricks for your specific needs. It's like having a team of language experts ready to help you with your AI projects.

Features

• Model Hub: Imagine a giant library filled with different language models, each with its own special skills. You can choose the one that best suits your needs.
• Easy-to-use tools: The library provides simple tools to help you work with these models, even if you're not a coding expert.
• Tokenizers: These are like special dictionaries that help the models understand the meaning of words and sentences.
• Pipelines: Think of these as pre-built workflows that simplify common language tasks.
• Community Support: A large and helpful community of users and developers is there to offer guidance and support.

Usage in AI Development

Hugging Face Transformers has become incredibly popular in AI development because it:

• Saves time: You can use pre-trained models instead of spending months training your own.
• Improves performance: These models are often much better than anything you could build yourself.
• Reduces costs: Fine-tuning an existing model is much cheaper than training a new one.
• Makes AI more accessible: It allows more people to build advanced language-based AI applications.

Benefits of learning Hugging Face Transformers

• Gain valuable skills: Experts in this technology are in high demand.
• Build amazing AI applications: Create AI that can understand and respond to human language.
• Stay ahead of the curve: Transformers are at the cutting edge of AI research.
• Join a thriving community: Connect with other AI enthusiasts and learn from their experience.

By learning Hugging Face Transformers, you'll be equipped to build the next generation of AI applications that can revolutionize how we interact with computers and information.

Learning Outcomes

By the end of the course, learners will:

• Understand transformer architectures and their applications.
• Use Hugging Face tools to load, fine-tune, and deploy models.
• Solve real-world NLP problems with pretrained transformers.
• Optimize performance for both training and inference.

Uplatz Certificate of Completion

Digital certificate - Included

Hugging Face Transformers for NLP and LLM Applications - Course Syllabus

Module 1: Introduction to Transformers and Hugging Face

1. Understanding Transformers

   • Evolution of NLP: From RNNs to Transformers
   • Transformer architecture overview
   • Applications of transformers in NLP, CV, and beyond

2. Introduction to Hugging Face

   • Overview of the Hugging Face ecosystem
   • Key libraries: Transformers, Datasets, Tokenizers
   • Installing and setting up Hugging Face

3. Exploring Pretrained Models

   • Concept of pretrained models
   • Overview of BERT, GPT, RoBERTa, T5, and others
   • Hugging Face Model Hub

Module 2: Tokenization and Data Preparation

1. Understanding Tokenization

   • WordPiece, BPE, and SentencePiece tokenization
   • Using Hugging Face's Tokenizers library

2. Preparing Data for Transformers

   • Loading datasets with the datasets library
   • Tokenizing text for transformers
   • Handling large datasets efficiently

Module 3: Fine-Tuning Pretrained Models

1. Fine-Tuning Concepts

   • Transfer learning in NLP
   • Overview of fine-tuning strategies

2. Text Classification

   • Fine-tuning BERT for sentiment analysis
   • Evaluating model performance

3. Named Entity Recognition (NER)

   • Training a model for NER tasks
   • Metrics for sequence labeling

4. Question Answering

   • Fine-tuning models for QA tasks
   • Using SQuAD datasets for QA

Module 4: Advanced Use Cases and Customization

1. Text Generation

   • Generating text with GPT and T5
   • Controlling text generation with parameters (temperature, top-k, top-p)

2. Summarization and Translation

   • Fine-tuning for abstractive summarization
   • Using translation models like mBART

3. Customizing Transformers

   • Modifying transformer architectures
   • Creating custom layers and modules

Module 5: Performance Optimization

1. Efficient Training Techniques

   • Mixed precision training with transformers and accelerate
   • Using the Trainer API for efficient training

2. Handling Large Models

   • Model parallelism and sharding
   • Using Hugging Face's BitsAndBytes for quantization

3. Serving Models in Production

   • Deploying models using Hugging Face Inference API
   • Optimizing inference with ONNX and TensorRT

Module 6: Multimodal Models and Future Trends

1. Introduction to Multimodal Models

   • Overview of CLIP, DALL·E, and other multimodal architectures

2. Fine-Tuning Multimodal Models

   • Practical applications: Image-text retrieval, visual question answering

3. Exploring Future Directions

   • Generative AI advancements
   • Large language models (LLMs) trends

Module 7: Capstone Project and Certification

1. Capstone Project

   • Define a project (e.g., building a custom chatbot, QA system, or summarization tool)
   • End-to-end implementation with Hugging Face tools

2. Certification Assessment

   • Multiple-choice quiz on key concepts
   • Practical coding assignment

Everyone

Passion and zeal to accomplish something big in your life!

• Machine Learning Engineer
• AI Research Scientist
• NLP Engineer
• Generative AI Engineer
• LLM and GPT Developer
• Data Scientist
• AI/ML Product Manager
• Deep Learning Engineer
• Data Engineer (with NLP specialization)
• Research Engineer (NLP focus)
• AI Solutions Architect
• NLP Consultant
• MLOps Engineer

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