Hugging Face Transformers Certification

Module 1: Introduction to Hugging Face

• Hugging Face ecosystem
• Transformers library
• Model Hub
• Datasets
• Tokenizers
• AI development workflow

Module 2: Transformer Fundamentals

• Transformer architecture
• Attention mechanisms
• Self-attention
• Encoder architecture
• Decoder architecture
• Encoder-decoder models

Module 3: Hugging Face Environment Setup

• Python environment
• Transformers installation
• PyTorch integration
• Notebook environments
• GPU concepts
• Project setup

Module 4: Hugging Face Model Hub

• Pretrained models
• Model discovery
• Model cards
• Model selection
• Model repositories
• Model loading

Module 5: Tokenization Fundamentals

• Tokens
• Vocabulary
• Subword tokenization
• Input IDs
• Attention masks
• Special tokens

Module 6: Working with Tokenizers

• AutoTokenizer
• Encoding text
• Decoding tokens
• Padding
• Truncation
• Batch tokenization

Module 7: Pretrained Transformer Models

• AutoModel
• Model architectures
• Model configuration
• Loading pretrained models
• Model outputs
• Inference workflows

Module 8: Hugging Face Pipelines

• Pipeline API
• Task selection
• Model configuration
• Batch inference
• Pipeline customization
• Practical workflows

Module 9: Text Classification

• Classification concepts
• Sentiment analysis
• Sequence classification
• Label mapping
• Prediction
• Classification pipelines

Module 10: Named Entity Recognition

• Token classification
• Entity extraction
• NER models
• Token labels
• Prediction pipelines
• Practical applications

Module 11: Question Answering

• Extractive question answering
• Context processing
• Question encoding
• Answer prediction
• QA pipelines
• Model evaluation

Module 12: Text Summarization

• Summarization concepts
• Extractive vs abstractive approaches
• Sequence-to-sequence models
• Generation parameters
• Summary generation
• Evaluation concepts

Module 13: Machine Translation

• Translation models
• Multilingual transformers
• Language pairs
• Tokenization
• Sequence generation
• Translation workflows

Module 14: Text Generation

• Causal language modelling
• Prompt input
• Greedy decoding
• Beam search
• Sampling
• Generation parameters

Module 15: Hugging Face Datasets

• Datasets library
• Loading datasets
• Dataset exploration
• Dataset splits
• Dataset transformations
• Data preparation

Module 16: Preparing Data for Transformers

• Text cleaning
• Tokenization
• Label preparation
• Batching
• Data collators
• Training datasets

Module 17: Fine-Tuning Transformer Models

• Fine-tuning workflow
• Training configuration
• TrainingArguments
• Trainer API
• Validation
• Model checkpoints

Module 18: Advanced Fine-Tuning

• Learning rates
• Batch sizes
• Epochs
• Optimizers
• Schedulers
• Training optimization

Module 19: Parameter-Efficient Fine-Tuning

• PEFT concepts
• LoRA
• Adapter approaches
• Trainable parameters
• Memory efficiency
• Fine-tuning strategies

Module 20: Embeddings & Semantic Search

• Text embeddings
• Sentence representations
• Similarity measures
• Semantic search
• Retrieval workflows
• Embedding applications

Module 21: Large Language Models

• LLM fundamentals
• Decoder-based models
• Context windows
• Prompt processing
• Model inference
• LLM applications

Module 22: Prompt Engineering

• Prompt structure
• Zero-shot prompting
• Few-shot prompting
• Instruction prompting
• Prompt templates
• Prompt optimization

Module 23: Retrieval-Augmented Generation

• RAG fundamentals
• Document preparation
• Embedding generation
• Retrieval
• Context augmentation
• Response generation

Module 24: Model Evaluation

• Evaluation metrics
• Accuracy
• Precision and recall
• F1 score
• ROUGE concepts
• Task-specific evaluation

Module 25: Model Optimization

• Model size
• Memory optimization
• Quantization concepts
• Mixed precision
• Efficient inference
• Performance tuning

Module 26: Accelerate & Distributed Training

• Hugging Face Accelerate
• Hardware acceleration
• Multi-GPU concepts
• Distributed training
• Training efficiency
• Scaling workflows

Module 27: Model Sharing & Versioning

• Model repositories
• Saving models
• Uploading models
• Version management
• Model documentation
• Collaboration workflows

Module 28: Deployment & Inference

• Production inference
• Inference endpoints
• API integration
• Batch inference
• Real-time inference
• Deployment architecture

Module 29: Responsible Transformer Development

• Model bias
• Dataset quality
• Hallucinations
• Model limitations
• Privacy considerations
• Responsible AI practices

Module 30: Practical Transformers Project

• Dataset preparation
• Model selection
• Fine-tuning
• Model evaluation
• Optimization
• Application deployment