Metadata-Version: 2.1
Name: text_explainability
Version: 0.3.5
Summary: Generic explainability architecture for text machine learning models
Home-page: https://git.science.uu.nl/m.j.robeer/text_explainability
Author: Marcel Robeer
Author-email: m.j.robeer@uu.nl
License: GNU LGPL v3
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

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  <img src="https://i.ibb.co/xXtJ23n/Text-Logo-Logo-large.png">
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_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 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


