metadata
language:
- pl
license: mit
tags:
- bert
- sentiment-classification
- clarinpl-embeddings
- LEPISZCZE
datasets:
- clarin-pl/aspectemo
metrics:
- accuracy
- f1
- precision
- recall
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}
}