Fine-Tuning for Natural Language Processing (NLP)

• Basic understanding of NLP concepts
• Experience with Python programming
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch

Audience

• Data scientists
• NLP engineers

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.

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