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Mastering Transformers

You're reading from   Mastering Transformers The Journey from BERT to Large Language Models and Stable Diffusion

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781837633784
Length 462 pages
Edition 2nd Edition
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Authors (2):
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Savaş Yıldırım Savaş Yıldırım
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Savaş Yıldırım
Meysam Asgari- Chenaghlu Meysam Asgari- Chenaghlu
Author Profile Icon Meysam Asgari- Chenaghlu
Meysam Asgari- Chenaghlu
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Table of Contents (25) Chapters Close

Preface 1. Part 1: Recent Developments in the Field, Installations, and Hello World Applications
2. Chapter 1: From Bag-of-Words to the Transformers FREE CHAPTER 3. Chapter 2: A Hands-On Introduction to the Subject 4. Part 2: Transformer Models: From Autoencoders to Autoregressive Models
5. Chapter 3: Autoencoding Language Models 6. Chapter 4: From Generative Models to Large Language Models 7. Chapter 5: Fine-Tuning Language Models for Text Classification 8. Chapter 6: Fine-Tuning Language Models for Token Classification 9. Chapter 7: Text Representation 10. Chapter 8: Boosting Model Performance 11. Chapter 9: Parameter Efficient Fine-Tuning 12. Part 3: Advanced Topics
13. Chapter 10: Large Language Models 14. Chapter 11: Explainable AI (XAI) in NLP 15. Chapter 12: Working with Efficient Transformers 16. Chapter 13: Cross-Lingual and Multilingual Language Modeling 17. Chapter 14: Serving Transformer Models 18. Chapter 15: Model Tracking and Monitoring 19. Part 4: Transformers beyond NLP
20. Chapter 16: Vision Transformers 21. Chapter 17: Multimodal Generative Transformers 22. Chapter 18: Revisiting Transformers Architecture for Time Series 23. Index 24. Other Books You May Enjoy

FastAPI Transformer model serving

There are many web frameworks we can use for serving. Sanic, Flask, and FastAPI are just some examples. However, FastAPI has recently gained a lot of attention because of its speed and reliability. In this section, we will use FastAPI and learn how to build a service according to its documentation. We will also use pydantic to define our data classes. Let’s begin!

  1. Before we start, we must install pydantic and FastAPI:
    $ pip install pydantic
    $ pip install fastapi
  2. The next step is to make the data model for decorating the input of the API using pydantic. But before forming the data model, we must know what our model is and identify its input.

    We are going to use a question-answering (QA) model for this. As you know from Chapter 6, the input is in the form of a question and a context.

  3. By using the following data model, you can make the QA data model:
    from pydantic import BaseModel
    class QADataModel(BaseModel):
        ...
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