Master fine-tuning Transformers, comparing deep learning architectures, and deploying sentiment evaluation models
This project provides an in depth, step-by-step guide to fine-tuning a Transformer model for sentiment classification while taking you thru the complete Machine Learning pipeline.
Curious for more? The COMPLETE project repository awaits you within the Bibliography section at the top of this tutorial, where you may explore every detail hands-on.
We start by defining the issue and preparing data, then progress through constructing, training, and evaluating models.
The main target is on fine-tuning a Transformer model, but we also compare its performance with two traditional deep learning architectures, ensuring a well-rounded understanding of the methodologies involved.
Key concepts like deep learning architecture, metric interpretation, and deployment are highlighted throughout the project.
It is a comprehensive learning experience, designed to deepen your understanding of recent Machine Learning techniques.
The dataset used was sourced from carblacac/twitter-sentiment-analysis (Apache 2.0…
