Multi-Class Language Classification With BERT in TensorFlow
Chapters for each section of the video (preprocessing, model build, prediction) are in the video timeline.

Transformers have been described as the fourth pillar of deep learning [1], alongside the three big neural net architectures of CNNs, RNNs, and MLPs.

However, from the perspective of natural language processing - transformers are much more than that. Since their introduction in 2017, they've come to dominate a majority of NLP benchmarks - and continue to impress daily.

What I'm saying is, transformers are damn cool. And with libraries like HuggingFace's transformers - it has become too easy to build incredible solutions with them.

So, what's not to love? Incredible performance paired with the ultimate ease-of-use.

In this video, we'll work through building a multi-class classification model using transformers - from start-to-finish.

🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5

Medium article:
https://towardsdatascience.com/multi-class-classification-with-transformers-6cf7b59a033a

Free access:
https://towardsdatascience.com/multi-class-classification-with-transformers-6cf7b59a033a?sk=544872025c2283c54cf4294814b8cae3

Link to Kaggle video:
https://youtu.be/DgGFhQmfxHo

[1] Fourth Pillar of AI:
https://ark-invest.com/articles/analyst-research/transformers-comprise-the-fourth-pillar-of-deep-learning/

00:00 Intro
01:21 Pulling Data
01:47 Preprocessing
14:33 Data Input Pipeline
24:14 Defining Model
33:29 Model Training
35:36 Saving and Loading Models
37:37 Making Predictions