text-explainability 0.3.6

Generic explainability architecture for text machine learning models

A generic explainability architecture for explaining text machine learning models.

Marcel Robeer, 2021

Installation

Method	Instructions
pip	Install from PyPI via pip3 install text_explainability.
Local	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.

Releases

text_explainability is officially released through PyPI.

See CHANGELOG.md for a full overview of the changes for each version.

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 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