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Hugging Face Transformers: Introduction to Pretrained Models

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Overview

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Learn how to build natural language processing (NLP) applications with pretrained transformers in Hugging Face, the popular machine learning platform.

Syllabus

Introduction
  • Building NLP apps with Transformers
  • Setting up the exercise files
1. Question-Answering (Qu-An)
  • Question-answering in NLP
  • Types of question-answering
  • Building a Qu-An pipeline
  • Evaluating Qu-An performance
2. Text Summarization
  • Text summarization in NLP
  • The BART model architecture
  • Summarization with pipelines
  • The ROUGE score
  • Evaluating with ROUGE
3. Natural Language Generation
  • Natural language generation (NLG) in NLP
  • Content creation with Transformers
  • Conversation generation
  • Chatbot conversation example
  • Machine translation in NLP
  • Translating with Hugging Face Transformers
4. Customizing Models with Transfer Learning
  • Training a custom model
  • Loading a Hugging Face dataset
  • Encoding and preprocessing the dataset
  • Customizing the model architecture
  • Training the sentiment model
  • Predicting with the custom model
5. Deploying and Using Hugging Face Models
  • Inference challenges with Transformers
  • Customizing pretrained models
  • Model compression overview
  • Serving multiple models
Conclusion
  • Continuing with Hugging Face

Taught by

Kumaran Ponnambalam

Reviews

4.3 rating at LinkedIn Learning based on 6 ratings

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