Sentiment Analysis with Deep Learning using BERT.
Preprocess and clean data for BERT Classification. Load in pretrained BERT with custom output layer. Train and evaluate finetuned BERT architecture on your own problem statement.
In this project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge.
SKILLS YOU WILL DEVELOP: Neuro-Linguistic Programming, Deep Learning, Machine Learning, Sentiment Analysis, BERT
Introduction to BERT and the problem at hand. Exploratory Data Analysis and Preprocessing. Training/Validation Split. Loading Tokenizer and Encoding our Data. Setting up BERT Pretrained Model. Creating Data Loaders. Setting Up Optimizer and Scheduler. Defining our Performance Metrics. Creating our Training Loop. Loading and Evaluating our Model.
Note: The project is supported in both Mac and Windows environments. Project files in Mac start with an "-". Unfortunately Python code file for Mac ._Complete.ipynb is not opening in Git.