Step By Step Guide To Implement Multi-Class Classification With BERT & TensorFlow

Bidirectional Encoder Representations from Transformers or BERT is a very popular NLP model from Google known for producing state-of-the-art results in a wide variety of NLP tasks. The importance of Natural Language Processing (NLP) is profound in the artificial intelligence domain.  The most abundant data in the world today is in the form of texts. That’s why having a powerful text-processing system is critical and is more than just a necessity. In this article, we will look at implementing a multi-class classification using BERT. The BERT algorithm is built on top of 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. LSTM network is a good example for seq2seq model. The transformer architecture is also responsible for transforming a sequence into another, but without depending