Learn HUGGINGFACE-TRANSFORMERS with Real Code Examples

Learn Python and PyTorch/TensorFlow basics

Understand transformers and attention mechanisms

Explore Hugging Face Tokenizers and datasets

Fine-tune pre-trained models on custom tasks

Deploy models using pipelines, ONNX, or cloud services

Week 1: Basic tokenization and pre-trained models

Week 2: Fine-tuning small NLP models

Week 3: Sequence-to-sequence tasks (summarization, translation)

Week 4: Advanced tasks like multi-task learning

Week 5: Production deployment and optimization

Explain the transformer architecture

How does attention work in Transformers?

What is a tokenizer and why is it important?

Difference between AutoModel, AutoModelForSequenceClassification, and pipeline

How do you optimize large transformer models for inference?

Tokenizer = converts text to tokens

AutoModel = loads pre-trained transformer

Pipeline = high-level task abstraction

Trainer = training and evaluation utility

Hub = repository for pre-trained models

Natural Language Processing with Transformers

Transformers for NLP

Hugging Face Transformers in Action

Practical Natural Language Processing with Transformers

Deep Learning for NLP with Transformers

Official Hugging Face tutorials

Jupyter notebooks and examples

Fast.ai courses using Transformers

Community blogs and workshops

Example projects on GitHub

https://huggingface.co/transformers/

https://huggingface.co/docs

https://github.com/huggingface/transformers

Hugging Face GitHub repository

Hugging Face forums

StackOverflow

Reddit /r/MachineLearning and /r/LanguageTechnology

YouTube tutorials and walkthroughs

Hugging Face GitHub repository

Hugging Face forums and discussions

StackOverflow

Reddit /r/MachineLearning and /r/LanguageTechnology

Tutorials and community blogs