Summary

In this chapter, we learned about text representation methods. We learned how it is possible to perform tasks such as zero-, few-, and one-shot learning using different and diverse semantic models. We also learned about NLI and its importance in capturing the semantics of text. Moreover, we looked at some useful use cases such as semantic search, semantic clustering, and topic modeling using Transformer-based semantic models. We learned how to visualize the clustering results and understood the importance of centroids in such problems. We also described instruction-tuned multitask models that can create representations according to the given instructions.

In the next chapter, you will learn about efficient Transformer models. You will learn about distillation, pruning, and quantizing Transformer-based models. You will also learn about different and efficient Transformer architectures that make improvements to computational and memory efficiency, as well as how to use them...