🤗 Hugging Face Transformers with Python – First Edition

Welcome to the Hugging Face Transformers with Python course! This comprehensive guide is designed to help you master state-of-the-art Natural Language Processing (NLP) using the Hugging Face transformers library and Python.

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📘 Course Overview

This course covers the fundamentals and advanced concepts of using Hugging Face Transformers for modern NLP tasks. You’ll learn everything from tokenization and fine-tuning to deployment.

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🧠 Prerequisites

• Python 3.7+
• Basic understanding of machine learning and NLP
• Familiarity with PyTorch or TensorFlow (not mandatory but helpful)
• Jupyter Notebook (recommended)

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🗂 Course Breakdown

📦 1. Introduction to Hugging Face and Transformers

• What are Transformers?
• Overview of Hugging Face ecosystem
• Installing transformers, datasets, and tokenizers
• Setting up GPU/Colab environment

🔤 2. Tokenization

• Understanding tokenizers
• Using AutoTokenizer
• Padding, truncation, attention masks
• Byte-Pair Encoding (BPE), WordPiece, Unigram

🧠 3. Pretrained Models

• Overview of model architectures (BERT, GPT, T5, DistilBERT, etc.)
• Using AutoModel, AutoModelForSequenceClassification, etc.
• Loading and testing models from the 🤗 Hub

🧾 4. Text Classification

• Sentiment analysis with BERT
• Training a classifier with Trainer API
• Evaluating model performance (accuracy, F1, etc.)

❓ 5. Question Answering

• Using pipeline for QA
• Fine-tuning BERT on SQuAD
• Using context and questions effectively

🗣 6. Text Generation

• Generating text using GPT-2 / GPT-3
• Sampling techniques: top-k, top-p (nucleus), temperature
• Prompt engineering basics

🧩 7. Named Entity Recognition (NER)

• Token classification with BERT
• Label alignment with subword tokens
• Training on CoNLL or custom datasets

🔁 8. Translation and Summarization

• Using MarianMT, T5, and BART
• Translation between languages
• Summarizing long documents with Transformers

🛠 9. Custom Datasets and Tokenizers

• Using datasets library
• Processing and tokenizing custom CSV, JSON, text
• Mapping dataset for training

🎓 10. Fine-Tuning Transformers (Advanced)

• Custom training loops with PyTorch
• Gradient accumulation and mixed precision
• Using Hugging Face Trainer and TrainingArguments

🚀 11. Model Deployment

• Saving and sharing models on Hugging Face Hub
• Deploying with transformers, Gradio, and FastAPI
• Inference optimization with ONNX and optimum

🧪 12. Evaluation and Experiment Tracking

• Using evaluate and accelerate
• Logging with TensorBoard, Weights & Biases
• Performance monitoring and reproducibility

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💾 Tools & Libraries Used

• transformers
• datasets
• evaluate
• tokenizers
• torch / tensorflow
• accelerate
• Gradio / Streamlit
• scikit-learn

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📁 Folder Structure (if this is a code repo)

huggingface-transformers-course/
│
├── notebooks/                  # Jupyter Notebooks for each lesson
├── datasets/                   # Sample and custom datasets
├── models/                     # Saved/Exported models
├── utils/                      # Helper functions
├── requirements.txt            # Python dependencies
└── README.md                   # This file