AI Sentiment Analysis with PyTorch and Hugging Face Transformers
33mBeginner2025-04-01
Authors
Zhongyu Pan
Content Creator at LinkedIn
Course details
This beginner course introduces sentiment analysis using Hugging Face Transformers and PyTorch. Instructor Zhongyu Pan illustrates the lessons by guiding you through building a model that classifies movie reviews as positive or negative. Learn how to prepare text data, fine-tune a pre-trained model, and evaluate model performances. By the end of the course, you’ll be better positioned to build and deploy a sentiment analysis model for real-world applications.
Learning objectives
Understand sentiment analysis and learn about its real-world applications.
Set up Hugging Face and PyTorch and use them for a sentiment analysis project.
Transform raw text data into a tokenized format for deep learning model training.
Fine-tune DistilBERT on a sentiment dataset using Trainer API.
Assess model performance with evaluation metrics: accuracy, precision, recall, and F1.
Learning objectives
Understand sentiment analysis and learn about its real-world applications.
Set up Hugging Face and PyTorch and use them for a sentiment analysis project.
Transform raw text data into a tokenized format for deep learning model training.
Fine-tune DistilBERT on a sentiment dataset using Trainer API.
Assess model performance with evaluation metrics: accuracy, precision, recall, and F1.
Skills covered
Hugging FacePyTorchNatural Language Processing (NLP)Artificial Intelligence (AI)Open SourceOne-Off
Concepts
0. Introduction
- 01 - Learn AI sentiment analysis
1. Introduction to Sentiment Analysis
- 02 - Prerequisites and learning goals
- 03 - What is sentiment analysis
- 04 - Popular applications of sentiment analysis
2. Getting Started with Hugging Face and PyTorch
- 05 - Overview of Hugging Face Transformers
- 06 - Introduction to PyTorch
- 07 - Setting up the environment on Google Colab
3. Data Preparation and Tokenization
- 08 - Loading dataset
- 09 - Preprocessing text data
- 10 - Tokenization
- 11 - Padding and truncation
4. Fine-Tuning DistilBERT for Sentiment Classification
- 12 - What is DistilBERT
- 13 - Setting up the model
- 14 - Configuring training parameters
- 15 - Training the model
5. Evaluating Model Performance
- 16 - Introduction to evaluation metrics
- 17 - Calculating accuracy, precision, recall, and F1 score
- 18 - Analyzing results
6. Project - Making Predictions on New Text Data
- 19 - Let's build a prediction function
- 20 - Test the model yourself
- 21 - How to deal with edge cases
7. Next Steps in Natural Language Processing
- 22 - Advanced NLP applications
Conclusion
- 23 - Continue your AI sentiment analysis journey