Learn How to Use Transformers with HuggingFace and SpaCy

The content explains how to use Transformer models for Natural Language Processing tasks within the spaCy framework. It provides a brief overview of the Transformer architecture, focusing on encoder-based models like BERT and RoBERTa, and contrasts them with older word vector methods. The main part is a step-by-step tutorial on fine-tuning a RoBERTa model from Hugging Face for a text classification task using the TREC dataset. This guide includes Python code for data preparation and a detailed configuration file for launching the training process via the spaCy command-line interface.