Fine-tuning a BERT model with transformers

Thiago Martins provides a comprehensive guide to fine-tuning a BERT model using the Transformers library, focusing on setting up a custom dataset and utilizing the Trainer class for optimization. The tutorial employs a dataset derived from the COVID-19 Open Research Dataset Challenge to demonstrate the process of generating query-document features and relevant labels, followed by data splitting into training and validation sets. The guide outlines steps for creating BERT encodings, establishing a custom dataset class, and fine-tuning the model using BertForSequenceClassification, while highlighting the potential need for cross-validation due to a limited number of queries. Once training is complete, the model is exported in ONNX format for deployment, and the tutorial suggests running the code on platforms like Google Colab with GPU support for efficient processing.