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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

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