text-explainability 0.3.0

Generic explainability architecture for text machine learning models

Text Explainability

A generic explainability architecture for explaining text machine learning models.

Marcel Robeer, 2021

Installation

Install from PyPI via pip3 install text_explainability. Alternatively, clone this repository and install via pip3 install -e . or locally run python3 setup.py install.

Example usage

Run lines in example_usage.py to see an example of how the package can be used.

Maintenance

Contributors

• Marcel Robeer (@m.j.robeer)
• Michiel Bron (@mpbron-phd)

Todo

Tasks yet to be done:

• Add data sampling methods (e.g. representative subset, prototypes, MMD-critic)
• Implement local post-hoc explanations:
  • Implement Anchors
  • Implement Foil Trees + ability to turn any output into a binary classification problem (fact-foil encodings)
• Implement global post-hoc explanations
• Add support for regression models
• More complex data augmentation
  • Top-k replacement (e.g. according to LM / WordNet)
  • Tokens to exclude from being changed
  • Bag-of-words style replacements
• Add rule-based return type
• Write more tests