Building a Text Classification model using BiLSTM

Text classification is one of the fundamental tasks in NLP. Almost every NLP system uses text classification somewhere in its backend. For example - The intent classifier of a chatbot, named-entity recognition, auto-tagging, etc.

There are many approaches to this problem from statistical machine learning models (Logistic, Naive Bays, SVM, etc.) to high-end deep learning models (CNN, RNN, Transformers, etc.). This blog covers the practical aspects (coding) of building a text classification model using a recurrent neural network (BiLSTM). We will use Python and Jupyter Notebook along with several libraries to build an offensive language/text classification model.

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