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Duration 21 hours
Course Outline
Introduction to NLP Fine-Tuning
- Defining the concept of fine-tuning
- Advantages of fine-tuning pre-trained language models
- Survey of leading pre-trained models (GPT, BERT, T5)
Exploring NLP Tasks
- Sentiment analysis
- Text summarization
- Machine translation
- Named Entity Recognition (NER)
Environment Configuration
- Installation and setup of Python and required libraries
- Leveraging Hugging Face Transformers for NLP workflows
- Loading and inspecting pre-trained models
Implementing Fine-Tuning Strategies
- Data preparation for NLP tasks
- Tokenization and input structuring
- Applying fine-tuning for classification, generation, and translation
Performance Optimization
- Managing learning rates and batch sizes
- Applying regularization methods
- Measuring model effectiveness using relevant metrics
Practical Laboratory Sessions
- Fine-tuning BERT for sentiment analysis
- Fine-tuning T5 for text summarization
- Fine-tuning GPT for machine translation
Deployment of Fine-Tuned Models
- Model export and persistence
- Integration of models into application stacks
- Foundations of cloud-based model deployment
Common Pitfalls and Best Practices
- Preventing overfitting during the fine-tuning process
- Addressing imbalanced datasets
- Maintaining experiment reproducibility
Emerging Trends in NLP Fine-Tuning
- Newly emerging pre-trained models
- Progress in transfer learning for NLP
- Exploration of multimodal NLP applications
Summary and Recommended Next Steps
Requirements
- Foundational knowledge of NLP concepts
- Proficiency in Python programming
- Working familiarity with deep learning frameworks such as TensorFlow or PyTorch
Target Audience
- Data scientists
- NLP engineers
