BERT stands for Bidirectional Encoder Representation from Transformer. It is the state-of-the-art embedding model published by Google. It has created a major breakthrough in the field of NLP by providing greater results in many NLP tasks, such as question answering, text generation, sentence classification, and many more besides. One of the major reasons for the success of BERT is that it is a context-based embedding model, unlike other popular embedding models, such as word2vec, which are context-free.
First, let's understand the difference between context-based and context-free embedding models. Consider the following two sentences:
Sentence A: He got bit by Python.
Sentence B: Python is my favorite programming language.
By reading the preceding two sentences, we can understand that the meaning of the word 'Python' is different in both sentences. In sentence A, the word 'Python' refers to the snake, while in sentence B, the word &apos...
