This is the fourth part of the metric series where, we will discuss about evaluation of the ML/DL model using metric NLP model are little tricky to evaluate because the output of these model is text/sentence/paragraph. So, we have to check the syntactical, semantic and well as the context of the output of the model So we uses different types of techniques to evaluate these model.

Before we start to dig deep, lets have some basic intuition about NLP model. NLP deals with Natural Language Processing i.e. it deals with the text data. We all know that ML model take input as numerical value i.e. numeric tensor and give numeric output. So we need to convert these text data into numerical format for this we have various preprocessing techniques such as Bog of Words. Word2vector, Doc2vector, Term Frequency(TF), Inverse Term Frequency(ITF), Term Frequency-Inverse Term Frequency(TF-IDF) or you can do manually by various techniques. For now you don’t need to get carried away just assume that Text have been converted by some algorithm or method into numerical value and vice versa.

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