How-to Build a Transformer for Language Classification in TensorFlow
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How to build a transformer model for sentiment analysis (language classification) using HuggingFace's Transformers library in TensorFlow 2 with Python.

We cover the full process from downloading data all the way through to building and training the transformer model.

This is a multi-class classification problem using both TensorFlow and Transformers to build a multiclass sentiment classifier.

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Article version is here:
https://betterprogramming.pub/build-a-natural-language-classifier-with-bert-and-tensorflow-4770d4442d41

Or here (free link if you don't have Medium membership):
https://betterprogramming.pub/build-a-natural-language-classifier-with-bert-and-tensorflow-4770d4442d41?sk=346cd4ce5ee019c400835588b56d8574

Article extract:
"High-performance transformer models like BERT and GPT-3 are transforming a huge array of previously menial, language-based tasks, into the work of a few clicks, saving a lot of time.

In most industries, the newest wave of language optimization is just getting started — taking their first baby steps. But these seedlings are widespread, and sprouting quickly.

Much of this adoption is thanks to the incredibly low barrier-to-entry. If you know the basics of TensorFlow or PyTorch, and take a little time to get to grips with the Transformers library — you’re already halfway there.

With the Transformers library, it takes just three lines of code to initialize a cutting-edge ML model — a model built from the billions of research dollars spent by the likes of Google, Facebook, and OpenAI.

This article will take you through the steps to build a classification model that leverages the power of transformers, using Google’s BERT.

Transformers
- Finding Models
- Initializing
- Bert Inputs and Outputs
Classification
- The Data
- Tokenization
- Data Prep
- Train-Validation Split
- Model Definition
- Train"