transformers-tutorials

PyTorch Transformers Tutorials

Introduction

In 2018, the landscape of Natural Language Processing (NLP) underwent a significant transformation with the introduction of BERT and other Transformer models like RoBERTa and XLM. These innovative neural network architectures, which utilize transformers, brought the concept of transfer learning to NLP—similar to the advancements seen earlier in computer vision. By learning from vast amounts of text data, these models become highly adept at understanding language nuances, and they have been fine-tuned to specific tasks to achieve impressive results. Major technology companies like Google and Facebook spearheaded this trend, training models on extensive datasets before making them publicly available for customization through the open-source community.

The company Hugging Face has significantly simplified access to and fine-tuning of these models with their Python package, Transformers.

Motivation

Despite groundbreaking advancements, applying these technologies to real-world business problems remains a challenge due to the specialized knowledge required. Therefore, these tutorials have been designed to help users effectively leverage transformer technologies and various Python tools to fine-tune language models for specific tasks. The motivation behind these tutorials stems from the need to bridge the gap between advanced NLP models and practical implementation in diverse domains.

Acknowledgements go to several exceptional resources that have enabled these tutorials, including the work of the Hugging Face team and Abhishek Thakur, known for his enlightening YouTube videos.

Tutorials Offered

Several problem statements are covered in these tutorials, ensuring a comprehensive understanding of transformer model applications:

• Text Classification: Multi-Class
  Users can explore multi-class text classification through available notebooks on GitHub, Colab, and Kaggle.

• Text Classification: Multi-Label
  Delving into multi-label text classification is made easy with resources on GitHub, Colab, and Kaggle.

• Sentiment Classification (with Experiment Tracking)
  Sentiment analysis tutorials, which include experiment tracking via WandB, are accessible on GitHub and Colab.

• Named Entity Recognition (NER) with TPU Processing
  NER processes with TPU support are demonstrated in tutorials on GitHub, Colab, and Kaggle.

• Question Answering (Tutorials for this task are in progress and will be available soon.)

• Summary Writing (with Experiment Tracking)
  Explore summarization techniques with experiment tracking in tutorials available on GitHub, Colab, and Kaggle.

Directory Structure

1. data: Contains all the toy data used for fine-tuning.
2. utils: Houses miscellaneous scripts required for fine-tuning preparations.
3. models: Stores all artifacts generated after fine-tuning.

Further Watching/Reading

To deepen understanding of these topics, viewers can explore several informative resources:

• Video Resources

  • Introduction in Simple Terms
  • Transfer Learning in NLP
  • BERT Research Series from Chris McCormick AI

• Reading Materials

  • Transformers Documentation
  • Pytorch Documentation
  • Google AI Blog

Through these tutorials, individuals interested in NLP can bridge the gap between the technical capabilities offered by transformer models and the practical application of these technologies in their own projects and domains.