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AI Text Summarization with Hugging Face

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The Hugging Face AI community aspires to advance and democratize artificial intelligence through open source and open science. In this course, Janani Ravi guides you through using this powerful AI tool to summarize text. Learn how extractive text summarization works, as well as its intermediate representations. Explore evaluation metrics, and find out how to navigate and access information on the Hugging Face platform. Go over logging into Hugging Face, using the sumy library for extractive summarization, and using abstractive text summarization with the Hosted Inference API. Learn about sequence-to-sequence models, transformers, and how to use them in Hugging Face. Plus, dive into using a Hugging Face pipeline to perform actual summarization, fine-tuning a transformer model, and exploring several Hugging Face transformers.

This course was created by Janani Ravi. We are pleased to host this training in our library.


01 - Introduction
  • 01 - AI Text Summarization with Hugging Face

  • 02 - 1. Understanding Extractive Text Summarization
  • 01 - Prerequisites
  • 02 - Extractive text summarization
  • 03 - Intermediate representations for extractive summarization
  • 04 - Evaluation metrics for summaries

  • 03 - 2. Performing Extractive Text Summarization Using Hugging Face
  • 01 - Exploring Hugging Face
  • 02 - Signing up for Hugging Face
  • 03 - The sumy library for extractive summarization
  • 04 - Extractive text summarization on Hugging Face

  • 04 - 3. Understanding Abstractive Text Summarization
  • 01 - Abstractive text summarization
  • 02 - Abstractive summarization using the Hosted Inference API on Hugging Face

  • 05 - 4. Understanding Transformers
  • 01 - Sequence-to-sequence models
  • 02 - Attention in sequence-to-sequence models
  • 03 - A brief introduction to Transformers
  • 04 - Transformers in Hugging Face

  • 06 - 5. Performing Summarization Using a Hugging Face Pipeline
  • 01 - Using Colab to work with Hugging Face Transformers
  • 02 - Loading the CNN Daily Mail dataset
  • 03 - Cleaning text data
  • 04 - Generating summaries with Hugging Face Transformers
  • 05 - Evaluating summaries using ROUGE scores
  • 06 - Summarizing text and computing aggregate ROUGE scores

  • 07 - 6. Fine-Tuning a Transformer Model for Summarization
  • 01 - Understanding tokenizers
  • 02 - Fine-tuning the T5 small model
  • 03 - Pushing the model to the Hugging Face Hub
  • 04 - Summarizing text using the fine-tuned model

  • 08 - 7. Exploring Different Hugging Face Transformers for Summarization
  • 01 - Accessing the BBC dataset on Google Drive
  • 02 - Instantiating and cleaning the BBC News summaries dataset
  • 03 - Generating summaries using Pegasus
  • 04 - Generating multiple summaries and computing aggregate ROUGE scores
  • 05 - Generating summaries using BART
  • 06 - Computing ROUGE metrics for a set of summaries

  • 09 - Conclusion
  • 01 - Summary and next steps
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