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⭐ If you find this repository helpful, please consider giving it a ⭐ here on GitHub (click the star button in the top right corner) It's a quick way to show support for this openly available code. ⭐

Transformers in Action

This is the corresponding code for the book Transformers in Action

The book covers:

  • Part 1 Introduction to transformers
    • The need for transformers
    • A deeper look into transformers
  • Part 2 Transformers for fundamental NLP tasks
    • Text summarization
    • Machine translation
    • Text classification
  • Part 3 Advanced models and methods
    • Text generation
    • Prompting and zero to few shot learning
    • Multimodal models
    • Optimize and evaluate large language models
    • Ethical and responsible Tlarge language models

Instructions and Navigation

All of the code is organized into folders. Each folder starts with CH followed by the chapter number. For example, CH03. The notebooks are then organized as follows: ch03_text_summarization_eval.ipynb, where ch03 indicates the chapter and text_summarization_eval what is done in the notebook.

Virtual Envrionment

The provided bash script create_env.sh automates the process of creating a Python virtual environment using either conda or pipenv, installing the required packages from a requirements.txt file. To use the script run bash create_env.sh in your terminal on Microsoft Windows (with WSL), Apple macOS, or Linux operating systems.

NOTE: A virtual environment is not necessary for the notebooks in this repository, as they are designed to be run on a cloud service with GPU support. Therefore, the provided instructions for creating a virtual environment are more for reference and general guidance than a strict requirement.

Running the Notebooks

Every notebook contains buttons so that the notebook can be oppend and run on the chosen cloud service like this:

Open In Colab Gradient NOTE: As of the currently used version of Hugging Face datasets there is an issue on Kaggle, which is why it is, for now, not supported for the notebooks.

Each notebook is connected with this Github repo, meaning by running a notebook, it will automatically clone the repo, so you can easily access all resources outside the notebook. Like customs functions and classes as well as utility functions to automatically install the requirements per chapter:

!git clone https://github.com/Nicolepcx/Transformers-in-Action.git

current_path = %pwd
if '/Transformers-in-Action' in current_path:
    new_path = current_path + '/utils'
else:
    new_path = current_path + '/Transformers-in-Action/utils'
%cd $new_path

NOTE: You need to run the notebooks with a GPU.

Project structure

├── LICENSE
├── README.md             <- The top-level README for developers using this project.
├── CH02                  <- Per chapter folder with Jupyter notebooks.
    ├── [name].ipynb      <- Jupyter notebooks with naming as mentioned above.
├── CH03                  <- Per chapter folder with Jupyter notebooks.
...                       <- Same structure for all chapters.
├── utils                 <- Custom classes and functions and utility functions.
├── resources             <- Some miscellaneous resources such as the logo.