README.md

# bcms-bertic-frenk-hate

Text classification model based on `classla/bcms-bertic` and fine-tuned on the [FRANK 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 models and with the same dataset. 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|



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_args = {
        "num_train_epochs": 12,
        "learning_rate": 1e-5,
        "train_batch_size": 74}

model = ClassificationModel(
    "bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True,
    args=model_args
    
)

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])
```