BERT-Based Transformer Projects

Spam SMS Detection you own this product

This project is part of the liveProject series BERT-Based Transformer Projects
prerequisites
intermediate Python and PyTorch • basics of natural language processing
skills learned
loading and configuring pretrained DistilBERT model using Hugging Face • building and training a text classifier using PyTorch Lightning • validating the performance of the model and quantifying the results
Rohan Khilnani
1 week · 7-10 hours per week · ADVANCED
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liveProject This project is part of the liveProject series BERT-Based Transformer Projects liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $14.99 $29.99 you save $15 (50%)
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In this liveProject, you’ll use the DistilBERT variation of the BERT Transformer to detect and block occurrences of spam emails in a data set. You’ll utilize binary classification to determine whether an email is spam, or legitimate. The DistilBERT model uses knowledge distillation to highly reduce the size of the transformer model, thus optimizing time and resources. You’ll learn to use the HuggingFace library to load your data set, and fine-tune it to your task with PyTorch Lightning. You’ll also explore alternative training approaches that utilize novel APIs in the transformers library to fine-tune pre trained DistilBERT models. Every part of an NLP pipeline is covered, from preprocessing your data to remove symbols and numbers, to model training and validation using F1-scoring to assess the robustness of your pipeline.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Rohan Khilnani
Rohan Khilnani is a data scientist at Optum, United Health Group. He has filed two patents in the field of natural language processing and has also published a research paper on LSTMs with Attention at the COLING conference in 2018.

prerequisites

This liveProject is for intermediate Python and NLP practitioners who are interested in implementing pretrained BERT architectures and customizing them to solve real-world NLP problems. To begin this liveProject you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Intermediate PyTorch
  • Basics of Google Colab
TECHNIQUES
  • Basics of machine learning
  • Basics of neural networks
  • Basics of natural language processing

you will learn

In this liveProject, you will develop hands-on experience in building a text classifier using PyTorch Lightning and Hugging Face. You’ll also get practical experience working on GPUs in the Google Colab environment.

  • Working with Jupyter Notebook on Google Colab
  • Loading and preprocessing a text data set
  • Tokenizing data using pre-trained tokenizers
  • Creating dataloaders and tensor data sets
  • Loading and configuring pre-trained DistilBERT model using Hugging Face
  • Building and training a text classifier using PyTorch Lightning
  • Validating the performance of the model using F1 scoring

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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