/notebooks

Jupyter notebooks for the Natural Language Processing with Transformers book

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

Transformers Notebooks

This repository contains the example code from our O'Reilly book Natural Language Processing with Transformers:

book-cover

Getting started

You can run these notebooks on cloud platforms like Google Colab or your local machine. Note that most chapters require a GPU to run in a reasonable amount of time, so we recommend one of the cloud platforms as they come pre-installed with CUDA.

Running on a cloud platform

To run these notebooks on a cloud platform, just click on one of the badges in the table below:

Chapter Colab Kaggle Gradient Studio Lab
Introduction Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Text Classification Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Transformer Anatomy Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Multilingual Named Entity Recognition Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Text Generation Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Summarization Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Question Answering Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Making Transformers Efficient in Production Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Dealing with Few to No Labels Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Training Transformers from Scratch Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Future Directions Open In Colab Kaggle Gradient Open In SageMaker Studio Lab

Nowadays, the GPUs on Colab tend to be K80s (which have limited memory), so we recommend using Kaggle, Gradient, or SageMaker Studio Lab. These platforms tend to provide more performant GPUs like P100s, all for free!

Running on your machine

To run the notebooks on your own machine, first clone the repository:

git clone https://github.com/nlp-with-transformers/notebooks
cd notebooks-test

Next, you'll need to install a few packages that depend on your operating system and hardware:

Once you have install the above requirements, create a virtual environment and install the remaining Python dependencies:

conda create -n book python=3.8 -y && conda activate book
python
>>from install import *
>>install_requirements()
# Use the following to run Chapter 7
# install_requirements(is_chapter7)

Citations

If you'd like to cite this book, you can use the following BibTeX entry:

@book{tunstall2022natural,
  title={Natural Language Processing with Transformers: Building Language Applications with Hugging Face},
  author={Tunstall, Lewis and von Werra, Leandro and Wolf, Thomas},
  isbn={1098103246},
  url={https://books.google.ch/books?id=7hhyzgEACAAJ},
  year={2022},
  publisher={O'Reilly Media, Incorporated}
}