Fine-Tuning for Natural Language Processing (NLP) Training Course
Fine-tuning pre-trained models for NLP tasks enables developers to leverage powerful language representations for specific applications such as sentiment analysis, summarization, and machine translation. This course offers in-depth guidance on the fine-tuning process for models like GPT, BERT, and T5, covering key techniques and best practices for achieving high-performing NLP solutions.
This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to enhance their NLP projects through the effective fine-tuning of pre-trained language models.
By the end of this training, participants will be able to:
- Understand the fundamentals of fine-tuning for NLP tasks.
- Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
- Optimize hyperparameters for improved model performance.
- Evaluate and deploy fine-tuned models in real-world scenarios.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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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
Open Training Courses require 5+ participants.
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