LEPISZCZE-aspectemo-allegro__herbert-base-cased-v1
Description
Finetuned allegro/herbert-base-cased model on clarin-pl/aspectemo dataset.
Trained via clarin-pl-embeddings library, included in LEPISZCZE benchmark.
Results on clarin-pl/aspectemo
|
accuracy |
f1_macro |
f1_micro |
f1_weighted |
recall_macro |
recall_micro |
recall_weighted |
precision_macro |
precision_micro |
precision_weighted |
value |
0.952 |
0.368 |
0.585 |
0.586 |
0.371 |
0.566 |
0.566 |
0.392 |
0.606 |
0.617 |
Metrics per class
|
precision |
recall |
f1 |
support |
a_amb |
0.2 |
0.033 |
0.057 |
91 |
a_minus_m |
0.632 |
0.542 |
0.584 |
1033 |
a_minus_s |
0.156 |
0.209 |
0.178 |
67 |
a_plus_m |
0.781 |
0.694 |
0.735 |
1015 |
a_plus_s |
0.153 |
0.22 |
0.18 |
41 |
a_zero |
0.431 |
0.529 |
0.475 |
501 |
Finetuning hyperparameters
Hyperparameter Name |
Value |
use_scheduler |
True |
optimizer |
AdamW |
warmup_steps |
25 |
learning_rate |
0.0005 |
adam_epsilon |
1e-05 |
weight_decay |
0 |
finetune_last_n_layers |
4 |
classifier_dropout |
0.2 |
max_seq_length |
512 |
batch_size |
64 |
max_epochs |
20 |
early_stopping_monitor |
val/Loss |
early_stopping_mode |
min |
early_stopping_patience |
3 |
Citation (BibTeX)
@article{augustyniak2022way,
title={This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish},
author={Augustyniak, Lukasz and Tagowski, Kamil and Sawczyn, Albert and Janiak, Denis and Bartusiak, Roman and Szymczak, Adrian and Janz, Arkadiusz and Szyma{'n}ski, Piotr and W{\k{a}}troba, Marcin and Morzy, Miko{\l}aj and others},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={21805--21818},
year={2022}
}