In this article, we are going to implement an email class classification whether it is spam or nonspam using BERT. Columns 2,3,4 contain no important data and can be deleted. Also, we rename column v1 as "category" and v2 as "message". Here we have 4825 ham and 747 spam emails. Creating a new column, if the value is spam the value of this spam column will be 1 and for ham, it will be zero.

BERT is one of the most popular algorithms in the NLP spectrum known for producing state-of-the-art results in a variety of language modeling tasks. Built on top of transformers and seq-to-sequence models, the Bidirectional Encoder Representations from Transformers is a very powerful NLP model that has outperformed many. The state-of-the-art results that it produces on a variety of language-specific tasks are enough to show that it is indeed a big deal. The results come from its underlying architecture which uses breakthrough techniques such as seq2seq (sequence-to-sequence) models and transformers. The seq2seq model is a network that converts a given sequence of words into a different sequence and is capable of relating the words that seem more important.