Summary

In this chapter, we learned how transformers work. Furthermore, we learned about leveraging ViTs to perform image classification. We then learned about document understanding while learning about leveraging TrOCR for handwriting transcription and LayoutLM for key-value extraction from documents. Finally, we learned about visual question answering using the pre-trained BLIP2 model.

With this, you should be comfortable in tackling some of the most common real-world use cases, such as OCR on documents, extracting key-value pairs from documents, and visual question answering on an image (handling multimodal data). Furthermore, with the understanding of transformers, you are now in a good position to dive deep into foundation models in the next chapter.