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Build a Natural Language Classifier With Bert and Tensorflow
Apply cutting-edge transformer models to your language problems
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 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 OutputsClassification
- The Data
- Tokenization
- Data Prep
- Train-Validation Split
- Model Definition
- Train
