Hugging Face Official Natural Language Processing (NLP) Course

📌 What is this course?

Authored directly by the core engineering team behind Hugging Face, the Hugging Face NLP Course is the premier production curriculum for modern natural language processing and open-source language models.

This course goes far beyond surface-level pipeline calls. You will understand the mathematical mechanics of Byte-Pair Encoding (BPE) tokenization, load and fine-tune transformer models using PyTorch and the Trainer API, benchmark accuracy metrics, and push production models to the Hugging Face Hub with interactive Gradio demo Spaces.

All chapters feature one-click executable Google Colab notebooks provided free of charge.

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🎯 Who is this for?

• Software developers wanting to thoroughly master transformers, datasets, and accelerate.
• Machine learning engineers looking to fine-tune open weights (BERT, RoBERTa, Llama) on proprietary enterprise datasets.
• Practitioners building scalable inference and serving pipelines for open-source AI models.

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📚 Core Syllabus

1. Transformer Models Overview: Attention mechanisms, encoder-decoder architectures
2. Using the Transformers Library: Input tensors, model heads, and tokenizer configurations
3. Fine-Tuning a Pretrained Model: Trainer API workflows, compute metrics, PyTorch loops
4. Sharing Models and Tokenizers: Hugging Face Hub workflows and model documentation cards
5. Applied NLP Tasks: Named Entity Recognition (NER), translation, question answering (QA)
6. Advanced Tokenization: Training custom BPE and WordPiece tokenizers from scratch
7. Production Deployment: Deploying interactive Gradio apps on Hugging Face Spaces