Generic explainability architecture for text machine learning models
Project description
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
text_explainability-0.3.4.tar.gz
(21.8 kB
view hashes)
Built Distribution
Close
Hashes for text_explainability-0.3.4.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1a98f2d565402314983a545350eb30de388fd0f08bc69464e462250212d7f43b |
|
MD5 | 11dcdbf6c6d8613e63f07464501b29b7 |
|
BLAKE2b-256 | 016ab417e53d23e6375cccec6cf76f1a60d5410c4abd0a538ce174e32f30dfc6 |
Close
Hashes for text_explainability-0.3.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 849eaf207e9c77cdaa6d2a684828125e70f5a28b71408d8741225330390965f1 |
|
MD5 | f2a62f0085f7e2ddf6e9ffcfcba83b1f |
|
BLAKE2b-256 | 6c1bbaf3d1ee6026bed9d8acbd3c267d9e23c23f36e1da0abf185105283cc9e4 |