README.md

# sloberta-frenk-hate
Text classification model based on `EMBEDDIA/sloberta` and fine-tuned on the [FRANK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the slovenian 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": 14,
        "learning_rate": 1e-5,
        "train_batch_size": 21,
        }
```

## 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|
|---|---|---|
|sloberta-frenk-hate|0.7785|0.7764|
|EMBEDDIA/crosloengual-bert |0.7616|0.7585|
|xlm-roberta-base |0.686|0.6827|

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

`Sloberta` vs `crosloengual-bert`:

| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U test|0.00163|0.00108|
|Student t-test |0.000101|3.95e-05|

`Sloberta` vs `xlm-roberta-base`:

| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U test|0.00108|0.00108|
|Student t-test |9.46e-11|6.94e-11|