Hugging Face for Natural Language Processing (NLP) Training Course

Introduction

• Survey of NLP and its real-world applications
• Introduction to Hugging Face and its core features

Setting up the working environment

• Installation and configuration of Hugging Face

Understanding the Hugging Face Transformers library and Transformer Models

• Examining the structure and functionalities of the Transformers library
• Review of various Transformer models available within Hugging Face

Utilizing Hugging Face Transformers

• Loading and applying pretrained models
• Deploying Transformers across various NLP tasks

Fine-Tuning a Pretrained Model

• Preparing datasets for the fine-tuning process
• Fine-tuning a Transformer model for specific tasks

Sharing Models and Tokenizers

• Exporting and sharing trained models
• Using tokenizers for efficient text processing

Exploring the Hugging Face Datasets Library

• Overview of the Datasets library within Hugging Face
• Accessing and leveraging pre-existing datasets

Exploring the Hugging Face Tokenizers Library

• Comprehending tokenization techniques and their significance
• Utilizing tokenizers provided by Hugging Face

Executing Classic NLP Tasks

• Implementing standard NLP tasks using Hugging Face
• Tasks such as text classification, sentiment analysis, named entity recognition, and more.

Leveraging Transformer Models for Speech Processing and Computer Vision

• Expanding the application of Transformers beyond text-based tasks
• Applying Transformers to speech and image-related challenges

Troubleshooting and Debugging

• Addressing common issues and challenges when working with Hugging Face
• Strategies for effective troubleshooting and debugging

Building and Sharing Your Model Demos

• Designing and creating interactive model demonstrations
• Effectively sharing and showcasing your models

Summary and Next Steps

• Recap of key concepts and techniques covered
• Guidance on further exploration and resources for ongoing learning