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Master's Thesis: Explainability of Transformer Models in Authorship Verification(m/w/x)
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
- English – Business Fluent
Tools & Technologies
- Machine Learning
- NLP
- Transformer models
- Python
- PyTorch
- HuggingFace
Benefits
Flexible Working
- Independent work schedule management
Not a perfect match?
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Master's Thesis: Explainability of Transformer Models in Authorship Verification(m/w/x)
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
- English – Business Fluent
Tools & Technologies
- Machine Learning
- NLP
- Transformer models
- Python
- PyTorch
- HuggingFace
Benefits
Flexible Working
- Independent work schedule management
About the Company
Fraunhofer-Gesellschaft
Industry
Research
Description
Das Unternehmen ist eine der führenden Organisationen für anwendungsorientierte Forschung mit 76 Instituten in Deutschland.
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