A Guide to Text Preprocessing Using BERT

Various state-of-the-art NLP applications like sentiment analysis, question answering, smart assistance, etc. require a tremendous amount of data. This large amount of data can be directly fed to the machine learning model. Almost all the text-based applications require a lot of pre-processing with the textual data such as creating the embedding vectors from scratch using the word frequency counter. This consumes a lot of effort and time. To overcome this, transfer learning models are used now for all complex pre-processing tasks. Here, we just need to feed our raw text to the transfer learning model and the rest of the processes are taken care of by it.  In contrast to this, we are going to discuss one such transfer learning framework, BERT, in this article. We will see how to use