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---
language: "hr"
license: "cc-by-sa-4.0"
tags:
- text-classification
- hate-speech
widget:
- text: "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni'."
---
# bcms-bertic-frenk-hate
Text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the Croatian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).
## Fine-tuning hyperparameters
Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:
```python
model_args = {
"num_train_epochs": 12,
"learning_rate": 1e-5,
"train_batch_size": 74}
```
## Performance
The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.
| model | average accuracy | average macro F1 |
|----------------------------|------------------|------------------|
| bcms-bertic-frenk-hate | 0.8313 | 0.8219 |
| EMBEDDIA/crosloengual-bert | 0.8054 | 0.796 |
| xlm-roberta-base | 0.7175 | 0.7049 |
| fasttext | 0.771 | 0.754 |
From recorded accuracies and macro F1 scores p-values were also calculated:
Comparison with `crosloengual-bert`:
| test | accuracy p-value | macro F1 p-value |
|----------------|------------------|------------------|
| Wilcoxon | 0.00781 | 0.00781 |
| Mann Whithney | 0.00108 | 0.00108 |
| Student t-test | 2.43e-10 | 1.27e-10 |
Comparison with `xlm-roberta-base`:
| test | accuracy p-value | macro F1 p-value |
|----------------|------------------|------------------|
| Wilcoxon | 0.00781 | 0.00781 |
| Mann Whithney | 0.00107 | 0.00108 |
| Student t-test | 4.83e-11 | 5.61e-11 |
## Use examples
```python
from simpletransformers.classification import ClassificationModel
model = ClassificationModel(
"bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True,
)
predictions, logit_output = model.predict(['Ne odbacujem da će RH primiti još migranata iz Afganistana, no neće biti novog vala',
"Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni' "])
predictions
### Output:
### array([0, 0])
```
## 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",
}
```
and the dataset used for fine-tuning:
```
@misc{ljubešić2019frenk,
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
year={2019},
eprint={1906.02045},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}
```
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