# 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}
}
```

{"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'."}]}
transformers pytorch safetensors bert text-classification hate-speech hr arxiv:1906.02045 license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #hate-speech #hr #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

bcms-bertic-frenk-hate
======================


Text classification model based on 'classla/bcms-bertic' and fine-tuned on the FRENK dataset 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:


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: bcms-bertic-frenk-hate, average accuracy: 0.8313, average macro F1: 0.8219
model: EMBEDDIA/crosloengual-bert, average accuracy: 0.8054, average macro F1: 0.796
model: xlm-roberta-base, average accuracy: 0.7175, average macro F1: 0.7049
model: fasttext, average accuracy: 0.771, average macro F1: 0.754


From recorded accuracies and macro F1 scores p-values were also calculated:


Comparison with 'crosloengual-bert':


test: Wilcoxon, accuracy p-value: 0.00781, macro F1 p-value: 0.00781
test: Mann Whithney, accuracy p-value: 0.00108, macro F1 p-value: 0.00108
test: Student t-test, accuracy p-value: 2.43e-10, macro F1 p-value: 1.27e-10


Comparison with 'xlm-roberta-base':


test: Wilcoxon, accuracy p-value: 0.00781, macro F1 p-value: 0.00781
test: Mann Whithney, accuracy p-value: 0.00107, macro F1 p-value: 0.00108
test: Student t-test, accuracy p-value: 4.83e-11, macro F1 p-value: 5.61e-11


Use examples
------------


If you use the model, please cite the following paper on which the original model is based:


and the dataset used for fine-tuning:



TAGS
#transformers #pytorch #safetensors #bert #text-classification #hate-speech #hr #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #hate-speech #hr #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
