Summary
In this chapter, we have covered a variety of introductory topics and also got our hands dirty with the hello-world transformer application. On the other hand, this chapter plays a crucial role in terms of applying what has been learned so far to the upcoming chapters. So, what has been learned so far? We took a first small step by setting the environment and system installation. In this context, the anaconda package manager helped us to install the necessary modules for the main operating systems. We also went through language models, community-provided models, and tokenization processes. Additionally, we introduced multitask (GLUE) and cross-lingual benchmarking (XTREME), which enables these language models to become stronger and more accurate. The datasets library was introduced, which facilitates efficient access to NLP datasets provided by the community. Finally, we learned how to evaluate the computational cost of a particular model in terms of memory usage and speed...
