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FRFraunhofer-Gesellschaft

Master's Thesis: Explainability of Transformer Models in Authorship Verification(m/w/x)

Darmstadt
Part-timeOn-site
AI/ML
Data Science

Applying and adapting interpretability methods for advanced neural networks in authorship verification, creating visualizations at an application-oriented research institute. Strong Python skills with PyTorch/HuggingFace experience preferred, Machine Learning and NLP knowledge essential. Independent work schedule management.

Requirements

  • Knowledge in Machine Learning, ideally NLP and Transformer models
  • Very good Python skills, preferably PyTorch or HuggingFace experience
  • Motivation for current explainability approaches
  • Scientific interest in modern AI evaluation metrics

Tasks

  • Apply existing explainability methods to newer models
  • Adapt explainability methods as necessary
  • Extend methods for non-expert understanding
  • Create visualizations for method explainability
  • Automate extraction of important input data
  • Implement current authorship verification models
  • Research novel explainability methods
  • Implement novel explainability methods for transformers
  • Evaluate novel explainability methods in detail
  • Compare methods with existing state-of-the-art approaches
  • Conduct evaluations on standard datasets (e.g., PAN)

Education

  • Bachelor's degreeOR
  • Master's degree

Languages

  • EnglishBusiness Fluent

Tools & Technologies

  • Machine Learning
  • NLP
  • Transformer models
  • Python
  • PyTorch
  • HuggingFace

Benefits

Flexible Working

  • Independent work schedule management
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