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Course Outline
Introduction to NLP Fine-Tuning
- What is fine-tuning?
- Benefits of fine-tuning pre-trained language models
- Overview of popular pre-trained models (GPT, BERT, T5)
Understanding NLP Tasks
- Sentiment analysis
- Text summarization
- Machine translation
- Named Entity Recognition (NER)
Setting Up the Environment
- Installing and configuring Python and libraries
- Using Hugging Face Transformers for NLP tasks
- Loading and exploring pre-trained models
Fine-Tuning Techniques
- Preparing datasets for NLP tasks
- Tokenization and input formatting
- Fine-tuning for classification, generation, and translation tasks
Optimizing Model Performance
- Understanding learning rates and batch sizes
- Using regularization techniques
- Evaluating model performance with metrics
Hands-On Labs
- Fine-tuning BERT for sentiment analysis
- Fine-tuning T5 for text summarization
- Fine-tuning GPT for machine translation
Deploying Fine-Tuned Models
- Exporting and saving models
- Integrating models into applications
- Basics of deploying models on cloud platforms
Challenges and Best Practices
- Avoiding overfitting during fine-tuning
- Handling imbalanced datasets
- Ensuring reproducibility in experiments
Future Trends in NLP Fine-Tuning
- Emerging pre-trained models
- Advances in transfer learning for NLP
- Exploring multimodal NLP applications
Summary and Next Steps
Requirements
- Basic understanding of NLP concepts
- Experience with Python programming
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch
Audience
- Data scientists
- NLP engineers
21 Hours
