Summary

In this chapter, we learned about text representation methods. We learned how it is possible to perform tasks such as zero-/few-/one-shot learning using different and diverse semantic models. We also learned about NLI and its importance in capturing 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.

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 in NLP problems.