Hugging Face for Natural Language Processing (NLP) Training Course

Introduction

• Overview of NLP and its applications.
• Introduction to Hugging Face and its key features.

Setting up a working environment

• Installing and configuring Hugging Face.

Understanding the Hugging Face Transformers library and Transformer Models

• Exploring the Transformers library structure and functionalities.
• Overview of various Transformer models available in Hugging Face.

Utilizing Hugging Face Transformers

• Loading and using pretrained models.
• Applying Transformers for various NLP tasks.

Fine-Tuning a Pretrained Model

• Preparing a dataset for fine-tuning.
• Fine-tuning a Transformer model on a specific task.

Sharing Models and Tokenizers

• Exporting and sharing trained models.
• Utilizing tokenizers for text processing.

Exploring Hugging Face Datasets Library

• Overview of the Datasets library in Hugging Face.
• Accessing and utilizing pre-existing datasets.

Exploring Hugging Face Tokenizers Library

• Understanding tokenization techniques and their importance.
• Leveraging tokenizers from Hugging Face.

Carrying out Classic NLP Tasks

• Implementing common NLP tasks using Hugging Face.
• Text classification, sentiment analysis, named entity recognition, etc.

Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision

• Extending the use of Transformers beyond text-based tasks.
• Applying Transformers for speech and image-related tasks.

Troubleshooting and Debugging

• Common issues and challenges in working with Hugging Face.
• Techniques for troubleshooting and debugging.

Building and Sharing Your Model Demos

• Designing and creating interactive model demos.
• Sharing and showcasing your models effectively.

Summary and Next Steps

• Recap of key concepts and techniques learned.
• Guidance on further exploration and resources for continued learning.