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

Delivery Options

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