Comments (3)
Hi @PGijsbers,
Thanks for cool idea - we had them in our Telegram channel in russian, but I'm also going to post them here in releases tab :)
Alex
from lightautoml.
@PGijsbers Here they are:
Release notes for 0.2.11:
- Added variable importances calculation functions for
TabularAutoML
andTabularUtilizedAutoML
- example is inTutorial_2
. - Added new functionality to return predictions from all algorithms before the blender layer for Train (OOF preds) and Prediction (Test preds) stages. To do that you can use
return_all_predictions
andweighted_blender_max_nonzero_coef
params ingeneral_params
config section, example is here in cell 9 - Bug-fixes for custom metrics and
TabularUtilizedAutoML
custom config list setting (for example to make it work like model multistart) etc.
Release notes for 0.2.12:
- Added classififcation NLP models interpretation based on LIME.
- Better support for HuggingFace models for embeddings training and extraction.
- Bug-fixes for language argument in several embeddings extraction algos and tokenizers
Release notes for 0.2.13:
- Added regression NLP models interpretation based on LIME.
Release notes for 0.2.14-0.2.15:
- Bug-fixes based on user reviews in the core functionality and report generation.
Release notes for 0.2.16:
General part:
- Removing profiler functionality (
log_calls
library usage) makes available to useDataParallel
during Bert models training (to turn it on usemultigpu
param innn_params
)
NLP part:
- Migration to
gensim >=4
. - Added functionaly to force big NLP models calculation without GPU (Random LSTM, Pooled Bert, etc).
- Changed logic of default parameters selection process: removing NN from pipeline if no GPU available, language and Bert model propagates to
AutoNLP
andNN
viatext_params
, changed logic for embeddings size check inAutoNLP
- Added several normalization techniques for embeddings in
AutoNLP
module
Interpretation part:
- Minor changes in LIME part - now we can show AutoML prediction and the predictions scale colorbar.
- L2X local interpretation algorithm is now available for NLP models. It finds the most informative tokens for the target variable based on the AutoML model predictions by mutual information optimization. For more info please check this tutorial in the repo or the same in the nbviewer
from lightautoml.
Feel free to close the issue now, or whenever you add your first release notes :)
from lightautoml.
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