How-to Build a Transformer for Language Classification in TensorFlow скачать в хорошем качестве

How-to Build a Transformer for Language Classification in TensorFlow

🎁 Free NLP for Semantic Search Course: https://www.pinecone.io/learn/nlp 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. 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 Article version is here: https://betterprogramming.pub/build-a... Or here (free link if you don't have Medium membership): https://betterprogramming.pub/build-a... 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"