Course Outline

Advanced Natural Language Generation (NLG) Techniques Overview for Government

  • Review of fundamental NLG concepts for government
  • Introduction to advanced NLG methods for government
  • The role of transformers in modern NLG for government

Pre-trained Models for NLG for Government

  • Overview of popular pre-trained models (GPT, BERT, T5) for government applications
  • Fine-tuning pre-trained models for specific tasks within the public sector
  • Training custom models with large datasets for government use

Improving NLG Outputs for Government

  • Ensuring coherence and relevance in text generation for government communications
  • Controlling text length and content using advanced NLG methods for government documents
  • Techniques for reducing repetition and enhancing fluency in government publications

Ethical and Responsible NLG for Government

  • Understanding the ethical challenges of AI-generated content for government
  • Addressing biases in NLG models used by government agencies
  • Ensuring the responsible use of NLG technology for government operations

Hands-On with Advanced NLG Libraries for Government

  • Working with Hugging Face Transformers for NLG in government applications
  • Implementing GPT-3 and other state-of-the-art models for government projects
  • Generating domain-specific content using NLG for government reports and communications

Evaluating NLG Systems for Government

  • Techniques for evaluating NLG models in a government context
  • Automated evaluation metrics (BLEU, ROUGE, METEOR) for government use
  • Human evaluation methods for quality assurance in government documents

Future Trends in NLG for Government

  • Emerging techniques in NLG research relevant to government operations
  • Challenges and opportunities in NLG development for government agencies
  • Impact of NLG on industries and content creation within the public sector

Summary and Next Steps for Government

Requirements

  • Basic understanding of Natural Language Generation (NLG) concepts
  • Experience with Python programming
  • Familiarity with machine learning models

Audience for government

  • Data scientists
  • AI developers
  • Machine learning engineers
 14 Hours

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