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LLM Book 6 - T5/ Text-to-Text Transfer Transformer for NLP Tasks for Dummies

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Table of Contents

1. Introduction

1.1 What is T5 and why is it important?

1.2 What are text-to-text problems and how can they cover various NLP tasks?

1.3 What are the main components and architecture of T5?

1.4 How to install and use T5 in Python?

2. T5 for Text Classification

2.1 What is text classification and what are some examples?

2.2 How to formulate text classification as a text-to-text problem?

2.3 How to fine-tune and evaluate T5 for text classification?

2.4 How to use T5 for sentiment analysis, topic modeling, and spam detection?

3. T5 for Text Summarization

3.1 What is text summarization and what are some examples?

3.2 How to formulate text summarization as a text-to-text problem?

3.3 How to fine-tune and evaluate T5 for text summarization?

3.4 How to use T5 for extractive and abstractive summarization?

4. T5 for Text Generation

4.1 What is text generation and what are some examples?

4.2 How to formulate text generation as a text-to-text problem?

4.3 How to fine-tune and evaluate T5 for text generation?

4.4 How to use T5 for story writing, dialogue generation, and paraphrasing?

5. T5 for Question Answering

5.1 What is question answering and what are some examples?

5.2 How to formulate question answering as a text-to-text problem?

5.3 How to fine-tune and evaluate T5 for question answering?

5.4 How to use T5 for factoid, non-factoid, and conversational question answering? 6. T5 for Text Translation

6.1 What is text translation and what are some examples?

6.2 How to formulate text translation as a text-to-text problem?

6.3 How to fine-tune and evaluate T5 for text translation?

6.4 How to use T5 for machine translation, code translation, and style transfer?

7. Conclusion

7.1 What are the main advantages and limitations of T5?

7.2 What are the current and future research directions for T5?

7.3 How to apply T5 to your own NLP projects?

7.4 Where to find more resources and references for T5? 

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