Fine-tuning a BERT model for search applications

In the blog post by Thiago Martins, the focus is on fine-tuning BERT models for search applications, emphasizing the need for compatibility between training and serving encodings. In search applications, documents are pre-processed, while queries are processed in real-time, necessitating efficient encoding strategies. The article details a method to create independent BERT encodings using separate tokenizers for queries and documents, allowing for different maximum lengths and avoiding automatic padding and special tokens. This approach ensures that the training encodings align with the serving requirements, enhancing the model's applicability in real-world search engines. Martins also highlights the future development of tools, like pyvespa, to automate this process, making it easier for users to train and deploy BERT models without manual adjustments.