NatLibFi/Annif: Annif 0.45
Creators
- 1. @NatLibFi
- 2. @niwa
- 3. @siilisolutions
- 4. @UB-Mannheim
Description
This release includes a new omikuji backend to support tree-based extreme multilabel classification machine learning algorithms, which give a big improvement to the quality of the subject indexing results. The --backend-param/-p
option is introduced to the CLI train
and learn
commands (previously that option was only available for suggest
and eval
); the option can be used to override the parameters from the config file. Also Python 3.8 support is introduced - however, the nn_ensemble
backend requires TensorFlow 2.0, which is not yet available for Python 3.8. The Vowpal Wabbit ensemble backend has been removed, as the neural network ensemble has similar features and gives better results.
New features:
- #343/#366/#368/#371 Omikuji backend
- #250/#289 Support backend param option in train and learn commands
- #345/#370 Support for Python 3.8
Bug fixes:
- #369 Fix for spurious "analyzer setting is missing" errors under WSGI
- #360/#361 Launching Gunicorn
Improvements/Maintenance:
- #367 Disable unnecessary Drone build dryruns for pushes
- #365 Remove vw_ensemble backend
- #359 Refactor backend project
- #358 Mauiserver dockerization
Files
NatLibFi/Annif-v0.45.0.zip
Files
(655.2 kB)
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Additional details
Related works
- Is supplement to
- https://github.com/NatLibFi/Annif/tree/v0.45.0 (URL)