Description:

Natural language processing $($NLP$)$ is a field of study which tries to make computers "understand" natural human language. There are tons of applications of NLP nowadays, such as speech recognition, machine translation, and chatbots or language models. One of the simplest NLP models $($but also one of the most useful$)$ is called the bag$-$of$-$words model.

In a bag$-$of$-$words model, take a collection of text $($referred to as a "document"$)$ and simply count how many times each unique word appears in the document. The resulting word count is the "bag$-$of$-$words," and can be thought of as a compressed representation of the original document. The bag$-$of$-$words can then be used to$,$ for instance, calculate how similar two documents are, or train a document classifier. $($Usually there's some extra steps beyond just counting the words, such as removing stop words and stemming, but we will ignore those for simplicity.$)$

Your task will be to implement a bag$-$of$-$words model by using a map to keep track of the word counts. A map data structure associates one piece of data, called a key, with another piece of data, called a value. In this case, each unique word is a key, and each word's value is their frequency or count. You will also implement some functions to retrieve the map's keys and values.