Training Sentiment Model Using BERT and Serving It With Flask API

Learn to develop and deploy a high-accuracy sentiment analysis model using BERT and Flask in this comprehensive tutorial video. Explore the entire process, from creating data loaders and implementing a BERT model with the Transformers library to training the model and serving it via a Flask REST API. Achieve 93% accuracy on the IMDB 50K Movie Reviews dataset as you master techniques for sentiment analysis, deep learning, and model deployment. Gain hands-on experience with data preparation, model architecture, training procedures, and inference using Flask. Access provided resources, including the training dataset and BERT base uncased files, to follow along and implement your own sentiment analysis solution.

Introduction
Config File
Model File
Train Function
Data Serve
Item Function
BERT
Data Set
Loss Function
Evaluation Function
Training Function
Parameters
Training Steps
Optimizer
Scheduler
Save Model
Train Model
Train Engine