Learn how to build a multi-class language classification model using BERT and TensorFlow in this comprehensive 43-minute tutorial. Explore the power of transformers in natural language processing as you work through each step of the process, from data preprocessing to model training and prediction. Follow along with clearly defined chapters for each section, including data input pipeline creation, model definition, and saving/loading techniques. Gain insights into the significance of transformers in deep learning and their dominance in NLP benchmarks. Utilize the HuggingFace transformers library to create an efficient and high-performing solution for multi-class text classification tasks.
Overview
Syllabus
Intro
Pulling Data
Preprocessing
Data Input Pipeline
Defining Model
Model Training
Saving and Loading Models
Making Predictions
Taught by
James Briggs
Related Courses
-
Fine Tune BERT for Text Classification with TensorFlow
-
Sentiment Analysis with Deep Learning using BERT
-
Natural Language Processing (NLP) with BERT
-
Transformer Models and BERT Model
-
Natural Language Processing: NLP With Transformers in Python
-
Mastering Natural Language Processing (NLP) with Deep Learning
