README.md

---
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.

```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. (** 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",
}
```