Learn to build Toxic Question Classifier engine with BERT and TensorFlow 2.4
Build a strong foundation in Deep learning text classifiers with this tutorial for beginners.
Understanding of text classification
Learn word embeddings from scratch
Learn BERT and its advantages over other technologies
Leverage pre–trained model and fine–tune it for the questions classification task
Learn how to evaluate the model
User Jupyter Notebook for programming
Test model on real–world data
A Powerful Skill at Your Fingertips Learning the fundamentals of text classification h puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, have excellent documentation. Text classification is a fundamental task in natural language processing (NLP) world.
No prior knowledge of word embedding or BERT is assumed. I’ll be covering topics like Word Embeddings, BERT, and Glove from scratch.
Jobs in the NLP area are plentiful, and being able to learn text classification with BERT will give you a strong edge. BERT is state of art language model and surpasses all prior techniques in natural language processing.
Google uses BER for text classification systems. Text classifications are vital in social media. Learning text classification with BERT and Tensorflow 2.4 will help you become a natural language processing (NLP) developer which is in high demand.
Specification: Toxic Question Classification using BERT and Tensorflow 2.4
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