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---
language: "hr"
tags:
- text-classification
- sentiment-analysis
widget:
- text: "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni'."
---
# bcms-bertic-parlasent-bcs-ter
Text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the BCS Political Sentiment dataset.
## Fine-tuning hyperparameters
Fine-tuning was performed with `simpletransformers`. Beforehand a brief sweep for the optimal number of epochs was performed and the presumed best value was 9. Other arguments were kept default.
```python
model_args = {
"num_train_epochs": 9
}
```
## Performance
The same pipeline was run with two other transformer models and `fasttext` for comparison. Macro F1 scores were recorded for each of the 6 fine-tuning sessions and post festum analyzed.
| model | average macro F1 |
|---------------------------------|-------------------|
| bcms-bertic-parlasent-bcs-ter | 0.7941 ±0.0101 ** |
| EMBEDDIA/crosloengual-bert | 0.7709 ± 0.0113 |
| xlm-roberta-base | 0.7184 ± 0.0139 |
| fasttext + CLARIN.si embeddings | 0.6312 ± 0.0043 |
Two best performing models have been compared with the Mann-Whitney U test to calculate p-values (** denotes p<0.01).
## Citation
If you use the model, please cite the following paper on which the original model is based:
```
@inproceedings{ljubesic-lauc-2021-bertic,
title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian",
author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5",
pages = "37--42",
}
```
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